Loop 289 Corridor Study Phase I

Transcription

1 2008 Loop 289 Corridor Study Phase I Hongchao Liu, Pavan Evuri, and Hao Xu Multidisciplinary Research in Transportation Texas Tech University August 2008

2 TABLE OF CONTENTS LIST OF TABLES... III LIST OF FIGURES... IV INTRODUCTION... 1 PROJECT OBJECTIVES...1 METHODOLOGICAL APPROACH...2 SUMMARY OF RESEARCH RESULTS AND RECOMMENDATIONS...5 ORGANIZATION OF THE REPORT...8 MICROSCOPIC SIMULATION... 9 TRAFFIC MODEL IN VISSIM...10 NETWORK CODING DESCRIPTION OF THE NETWORK...12 NETWORK MODELING...14 TRAFFIC DATA...19 MODEL CALIBRATION AND VALIDATION...27 MODEL MODIFICATIONS...31 THE VISSM SIMULATION NETWORK AND RESEARCH OBJECTIVES INTRODUCTION...34 DIAMOND AND X PATTERN INTERCHANGES...36 THE ANALYSIS OF LEVEL OF SERVICE ON THE MAINLANES CURRENT CONDITION...38 SECTION BY SECTION EVALUATION OF THE IMPROVEMENT ALTERNATIVES...40 SUMMARY OF LOS FOR CURRENT CONDITION...51 THE LOS ANALYSIS FOR THE PROJECTED TRAFFIC...55 CONCLUSIONS REFERENCES ii

3 LIST OF TABLES Table 1 Section by section level of service under current condition... 5 Table 2 Section by section level of service as a result of Alternative Table 3 Section by section level of service as result of Alternative Table 4 Section by section level of service as result of Alternative Table 5 Observation table of the moving car method Table 6 Observation table for the correlation coefficient Table 7 Observation Table of the %RMSE Table 8 LOS of the basic network for current traffic volume Table 9 Comparison of LOS between Basic Network and Alternative Table 10 Comparison of LOS between Basic Network, Alternative 1 and Alternative 2 53 Table 11 Comparison of LOS between Basic Network and all alternatives Table 12 LOS of Basic Network for Current and Future Traffic Volumes Table 13 LOS of the Alternative1 for the current and future traffic demands Table 14 LOS of the Alternative 2 for the current and future traffic demands Table 15 LOS of the Alternative 3 for the current and future traffic demands iii

4 LIST OF FIGURES Figure1 Graphs defined for the function of Maximum Acceleration Figure 2 Google Earth Image of the Project Area Figure 3 Coding of traffic control systems at University Avenue Figure 4 Snapshot of simulation at University Avenue Figure 5 Snapshotof the Signal Controller in VISSIM Figure 6 Locations on the network where the data were collected in phase Figure 7 Locations on the network where the data were collected in phase Figure 8 The observed counts and simulation volume Figure 9 X-Configurations at University Avenue and Indiana Avenue Figure 10 Conversion of X interchanges to Diamond at Quaker Avenue Figure 11 Five sections on the network where the LOS analysis is conducted Figure 12 Diamond pattern interchange at Indiana Avenue Figure 13 X pattern interchange at Slide Road overpass Figure 14 Lane distribution of current traffic on the basic network (westbound) Figure 15 Traffic Density of basic network for current traffic (westbound) Figure 16 Traffic volume and lane distribution at the Slide Road overpass as result of different alternatives (westbound) Figure 17 Traffic density at the Slide Road Overpass as result of different alternatives (westbound) Figure 18 Traffic volume and lane distribution at the section of Slide Road and Quaker Ave as result of different alternatives (westbound) Figure 19 Traffic density at the section of Slide Road and Quaker Ave as result of different alternatives (westbound) Figure 20 Traffic volume and lane distribution at the section Quaker Ave and Indiana Ave as result of different alternatives (westbound) Figure 21 Traffic density at the section of Quaker Ave and Indiana Ave as result of different alternatives (westbound) Figure 22 Traffic volume and lane distribution at the section of Indiana Ave and University Ave as result of different alternatives (westbound) Figure 23 Traffic density at the section of Indiana Ave and Universtiy Ave as result of different alternatives (westbound) Figure 24 Traffic volume and lane distribution at the section of University Avenue and IH27 as result of different alternatives (westbound) Figure 25 Traffic density at the section of University Avenue and IH-27 as result of different alternatives (westbound) Figure 26 Traffic Density of basic network for current traffic (eastbound) Figure 27 Traffic volume and lane distribution on basic network under current and forecasted traffic conditions (westbound) iv

5 Figure 28 Traffic density on the basic network for current and forecasted traffic volumes (westbound) Figure 29 Effect of Alternative 1 on traffic volume and lane distribution with current and projected traffic data (westbound) Figure 30 Effect of Alternative 1 on traffic volume and lane distribution with current and projected traffic data (westbound) Figure 31 Effect of Alternative 2 on traffic volume and lane distribution with current and projected traffic data (westbound) Figure 32 Effect of Alternative 2 on traffic volume and lane distribution with current and projected traffic data (westbound) Figure 33 Effect of Alternative 3 on traffic volume and lane distribution with current and projected traffic data (westbound) Figure 34 Effect of Alternative 4 on traffic volume and lane distribution with current and projected traffic data (westbound) v

6 INTRODUCTION Traffic congestion has been growing steadily in the areas of every size in the United States. Measures in all of the population size categories show that congestion now lasts a longer period of time and affects more of the transportation network in 2007 than in If trends do not change, medium-sized regions will soon have the traffic problems that large areas have now. Preventive action needs to be taken in not only metropolitan areas, but also medium-sized regions. Congestion in medium-sized cities has many unique features that are different with that in large metropolitan areas. The congestion won t be all-day long but the locations near key activity centers are relatively more vulnerable to temporal and spatial changes in traffic volumes. This situation is often worsened by the increased lane changing and weaving maneuvers at on and off ramp areas and nearby intersections with local arterial streets due to the closely-spaced interchanges. Lubbock, like other medium sized cities is facing an increasing need to serve the growing regional travel demand. To address this issue, the Lubbock District of Texas Department of Transportation (TxDOT), in collaboration with the City of Lubbock and the Lubbock Metropolitan Planning Organization (MPO) is implementing various strategies, including capacity expansions, advanced communication technologies, and effective operational management strategies to improve Lubbock s traffic condition. Project Objectives One of the projects sponsored by the Lubbock District office of TxDOT requires a thorough evaluation of the level of service of a 6-mile section of its highway loop system that encircles the City of Lubbock. It also requires identification of the best solutions from a set of improvement alternatives for both current and future traffic conditions. The area of interest is one of the busiest segments of the loop system, which stretches from the I-27 interchange on the east side of the south Loop 289 to Spur 327 on the west of the loop system. It traverses the key business center of the city and includes four major interchanges connecting with local arterial streets. Specifically, the study conducted through this research includes modeling the current level of service on the mainlanes of south Loop 289 and the performance of the following alternatives. 1

7 1. Keeping the existing ramp configurations, add an auxiliary lane to the outside mainlane between the entrance and exit ramps on the roadway segment between: o Slide Avenue and Quaker Avenue o Quaker Avenue and Indiana Avenue o Indiana Avenue and University Avenue and o University Avenue and I Convert the ramp configuration from diamond interchanges to X patterns along south Loop 289, both east and west bound between: o Quaker Avenue and Indiana Avenue o Indiana Avenue and University Avenue and o University Avenue and I-27 Depending on the simulation results, add a third lane on the frontage road connecting the exit and entrance ramps. 3. Alternative (2) with an additional auxiliary lane on the mainlanes going over the bridges. Operational challenges along the study corridor include peak-hour main-lane congestion, weaving problems between access points, frontage road congestion and increased accident frequency. Contributing to the operational problems are the design deficiencies such as the geometry and type of the interchanges, length of weaving sections, ramp tapers, and unevenly distributed traffic volumes on mainlanes. In addition, the closely spaced urban arterials intersecting with the loop system and intensive commercial development along the route also contribute to the problem. Methodological Approach The key requirement of this project involves identifying best solutions from various potential improvement alternatives, including adding auxiliary lanes, conversion from X/diamond type design to diamond/x types, and a combination of adding auxiliary lanes and changing the interchange types. While field evaluation provides real world assessment, traffic simulation is advantageous in conducting what if studies before implementation and before and after analysis in evaluation. 2

8 It is also a more economical way as compared to the cost of field evaluation. This study is conducted on the basis of microscopic simulation. It is a challenging endeavor to conduct such a comprehensive simulation analysis for the given network. For example, to simulate a signalized intersection in congruent with the real field conditions, a number of design and control parameters have to be addressed in the simulation. The design parameters include, but are not limited to the geometry of the intersection, signal heads and the detectors (in case of actuated traffic signals). The major control parameters include the logic of fixed time/actuated traffic signal under normal operation and during emergency preemption, communication link between the detectors and the signal, signal coordination, and priority design for moving vehicles. Several traffic simulation models have been developed for different purposes over the years, including VISSIM (PTV 2006), CORSIM (FHWA, 2006), and PARAMICS (Quastone, 2006). After careful screening, the VISSIM software was selected for this project due to its flexibility to large network simulation, enhanced realism in modeling actuated signals, and detailed modeling of travel behaviors of freeway drivers. The simulation work was performed in the following steps: 1. Network coding The relevant features of the test site were coded in the simulation based on the references provided by TxDOT, including the Sign and Striping Layouts and Geopak files of the network corridor. The basic geometry of the network was coded on an aerial photograph of the study area, which was downloaded from Google Earth. Links and connectors are the two design parameters which are used to develop the network in VISSIM. Links are used to define the mainlanes and the local arterial streets. Connectors are used at curves like turning movements and entrance and exit ramps. 2. Modeling control devices After coding the geometry of the network, control devices are defined in the simulation model. They are the parameters which control the traffic flow in the simulation model. These control devices include a variety of parameters including Signal Controllers, Stop signs, Priority rules, Desired Speed Decisions and Reduced Speed Decisions. 3

9 3. Calibration of the simulation model After all the features in the network are modeled, the model parameters are calibrated to make the simulation model replicate the real field conditions. The procedure by which the parameters of the model are adjusted so that the simulated response agrees with the measured field conditions is what is known as Model Calibration. Some of these parameters will have an effect on the driving behavior and some of them will have an effect on the speed and acceleration of the vehicle. All these model parameters can be categorized into Car following parameters Lane changing Parameters Kinetic Parameters Vehicle Parameters The calibration task was undertaken using the field data observed by the Transtech Lab and the data collected by TxDOT. Once the simulation model of the basic network is calibrated, it is used to develop the simulation models of the alternative improvement networks. The LOS analysis for the current and forecasted traffic conditions were then conducted, on the basis of the simulation model developed in VISSIM. The analysis work was performed in the following steps: 1. Analysis of current level of service (LOS) The level of service of the South Loop 289 was determined for the current traffic volumes. The LOS of the network was determined for five different sections, between the entrance and exit ramps along the network. The LOS of each section was determined using HCS 2000, a software package that follows the procedure defined in the Highway Capacity Manual Analysis of LOS with proposed improvement alternatives A set of simulation models representing the proposed improvement alternative networks are developed on the basis of the basic calibrated network. All these networks, including the basic network are modeled with the current traffic volumes and the analysis of LOS is conducted on the output volumes from the simulation networks. These volumes are converted to density for LOS analysis. 4

10 3. Analysis of LOS with the forecasted traffic volume After the analysis of the alternatives with current traffic volume, the current traffic volumes are forecasted for 5 years at an annual incremental rate of 3%. Then, all the simulation networks are modeled with the forecasted volumes to analyze the traffic conditions that prevail after 5 years, on each of the network. Summary of Research Results and Recommendations The LOS analysis is carried out for the basic and three alternative networks developed in the simulation arena. The basic network represents the existing condition, while Alternative 1 (A1) is developed by adding an auxiliary lane between each entrance and exit ramps on both Eastbound and Westbound directions; Alternative 2 (A2) is developed by changing the ramp configuration from Diamond to X- pattern and adding an additional lane on the frontage road between each exit and entrance ramp; and Alternative 3 (A3) is developed based on the Alternative 2, by adding an auxiliary lane on the mainlanes over the bridges. The simulation analysis is conducted at five sections including the Slide Road overpass (S1), Slide Road and Quaker Avenue (S2), Quaker Avenue and Indiana Avenue (S3), Indiana Avenue and University Avenue, and the section between University Avenue and IH27. The section by section level of service was first determined based on the morning peak traffic data on the basic network, which is shown in Table 1. Table 1 Section by section level of service under current condition Section 1 Section 2 Section 3 Section 4 Section 5 Westbound B B D D D Eastbound C B D D C Though none of the freeway segments has reached its capacity under current traffic conditions, the research also revealed that vehicles are not evenly distributed on the mainlanes. The traffic volume on the outside lane is much higher than that on the inside lane at some of the sections, leading to an undesirable situation that the level of service of the outside lane may be significantly lower than that shown in the table. For instance, the outside lane volume accounts for almost 50 percent of the total volume on the Slide Road overpass (Westbound), while the traffic on the inside lane (i.e., the median lane) accounts for only 24 percent. In this study, the 5

11 performance of the improvement alternatives is justified not only by their resulted level of service, but also by the effects to lane distribution of the vehicles. The three improvement strategies impact on the level of service of the network in different ways. Adding an auxiliary lane to the mainlanes between entrance and exit ramps, as in the case of Alternative 1 provides better LOS than the existing network because of the increased roadway capacity. However, it cannot result in a desirable distribution of traffics on the mainlanes. A driver s lane choice behavior is influenced, to a considerable extent, by the level of congestion and his travel distance. Traffic is likely to be evenly distributed on highway segments of high v/c ratio with high percentage of long distance travelers. For the south Loop area, traffic is not evenly distributed due to a combined effect of the following issues: a) Slide Road, Quaker Avenue, Indiana Avenue, University Avenue are the destinations of most trips; b) short trip travelers account for a considerable percentage; c) the v/c ratio is relatively low; and d) there are no exit ramps on the inside lane. Therefore, adding an auxiliary lane on the mainlanes between on and off ramps improves the level of service, as shown in Table 2, has no positive impact on lane distribution. Table 2 Section by section level of service as a result of Alternative 1 Section 1 Section 2 Section 3 Section 4 Section 5 Westbound B B B C C Eastbound C B B B C Changing the ramp configuration from Diamond to X pattern, as in the case of Alternative 2, has considerably reduced the volume of traffic on the mainlanes. Because an X- pattern interchange will transfer the vehicles with shorter Origin-Destination trips (O-D) on to the frontage road. As this has increased the traffic volume on the frontage roads, an additional lane can be added to the frontage road to accommodate the vehicles converted from mainlanes. This change in ramp configuration also increased the traffic volumes at the two sections: section between Slide & Quaker and at the Slide Road overpass. Because, the X-pattern interchange has provided an entrance ramp on to the mainlanes instead of exit ramp at the section between Slide and Quaker. The level of service resulted from the Alternative 2 is shown in Table 3. 6

12 Table 3 Section by section level of service as result of Alternative 2 Section 1 Section 2 Section 3 Section 4 Section 5 Westbound C B B C C Eastbound C B B B C The result is similar to that from Alternative 1, except that the level of service at section 1 (westbound) is one grade lower (C versus B). This is because the ramp configuration to the east of Quaker Avenue is changed from exit ramp to entrance ramp, leading to an increased volume on the Slide Road overpass. The slight change in LOS is compensated by better lane distribution as the change in ramp configuration decreased the number of vehicles with short O-D trips on the mainlanes. In Alternative 3, the ramp configuration remains the same as Alternative 2 throughout the network and an auxiliary lane is provided on the mainlanes over the bridges rather than in between the entrance and exit ramps. As shown in Table 4, though the level of service may be improved on the bridges (e.g., the Slide Road overpass), this change seems to encourage traffic on the frontage roads to move on to the mainlanes, leading to increases in volume and density on section two and three. Table 4 Section by section level of service as result of Alternative 3 Section 1 Section 2 Section 3 Section 4 Section 5 Westbound B C C C C Eastbound C C B B C All these three strategies will improve, to varying degrees, the level of service on the mainlanes of the corridor. In addition, the resulted level of service can be maintained almost at the same level for the forecasted traffic demand in five years. Their impacts on traffic lane distribution are not significant at both the current and forecasted demand level due to a combined efffect of high percentage of short-trip travellers on the corridor, low v/c ratio at some of the sections, and lack of exit ramps on the inside lanes. The Alternative 1 improves the level of service through added capacity on the mainlanes of the corridor. Though it is an effective way to reduce the density on mainlanes, this strategy may 7

13 not be needed at current time as the current and forecasted LOS level is relatively low on the mainlanes. The Alternative 2 alleviates the level of traffic density significantly on the mainlanes by converting the ramp configuration from diamond to X patter. The auxiliary lane is necessary on the frontage to accommodate the increased traffic volumes diverted from the mainlanes. Though the density on the bridges will be increased as a result of the extra traffic volumes brought by the entrance ramp, this strategy may also alleviate the level of congestion at the intersections. So, the effectiveness of A2 needs to be examined in conjunction with detailed evaluation of the frontage roads and the intersections. The Alternative 3 improves the level of service through a combined effect of changing ramp configuration and addition of the auxiliary lane on the bridges. The resulted performance is almost the same as that from Alternative 2 at current and forecasted traffic demands. Therefore, we do not recommend adding auxiliary lanes on the bridges at this stage considering the associated cost and the limited additional benefits as compared to that brought by Alternative 2. In summary, it is recommended that a) the ramp configuration be changed from diamond to X type along the south Loop corridor with an auxiliary lane added onto the frontage road; b) adding an auxiliary lane on the bridges may be considered later but not necessary at this stage; c) a systematic evaluation be conducted in conjunction with detailed analysis of level of service on the frontage roads and intersections. Organization of the report This report contains four sections. This section presents an overview of the project and provides detailed summaries of the findings. In the second section, the Vissim simulation tool used in the project is presented. In the third section, description and modeling of the network is presented along with the calibration and validation of the simulation model. Modeling of the network includes development of the network and coding of all control parameters needed for the simulation model. Section four depicts the simulation results and provides detailed LOS analysis of the proposed alternative strategies. 8

14 MICROSCOPIC SIMULATION Simulation is a software tool used to replicate the real traffic conditions of the field, to evaluate the operational conditions and to investigate the network. Microsimulation models are traffic models which are used to determine the driving behavior of individual vehicles traveling on the network. The Microsimulation software tools have built-in traffic models for carfollowing, lane changing and gap-acceptance. Microscopic simulation is mainly used for the evaluation and development of road traffic management and control systems. Use of Microsimulation models will provide a better and clear presentation of actual driver behavior inside the simulation network. These models are helpful to code complex traffic problems along with the implementation of intelligent transportation systems. Moreover these software packages have an edge, to show the traffic flow traversing on the networks, various road and junction types (Bloomberg and Dale, 2000). This helps to represent the problem and solution in a format, understandable to professionals and as well as laymen alike. Microsimulation can be used to develop new systems and optimize their effectiveness. They can determine the impact of a new alternative solution by producing outputs on a wide range of measures of effectiveness, which are very difficult and are almost impossible to estimate in the real field. Microscopic models are developed to simulate the traffic systems, which have their systems defined on a vehicle by vehicle basis. They update the parameters such as position, speed, acceleration, and lane position of the vehicles based on the time steps, and control parameters like traffic signals, signs and roadway geometrics. Detailed modeling of traffic signal operations is also generally included in Microscopic simulations. However, the design of the microscopic traffic simulation models is based upon the assumption that the driving behavior of the vehicles is safe, according to the gap acceptance and lane changing. The degree of user control developed in the simulation model depends on the parameters of the network that control the traffic movements in the network. These parameters include but not limited to roadway geometrics, type of signal controllers, priority rules, and will vary depending on the software program used. (ODOT, 2007). 9

15 Traffic model in Vissim VISSIM provides a discrete, stochastic and time step based microscopic model. In VISSIM, the driver and the vehicle are modeled as single entities. The traffic flow model in VISSIM provides a psycho-physical car following model along with the rule-based algorithm for lane changing movements (Fellendorf and Vortisch, 2005). The model is based on the model developed by Wiedemann, which is based upon the assumption that the driver can be in one of the four following modes: Free driving: This is the first kind of mode of driving behavior in which no influence of preceding vehicles is observable on the following vehicle. In this mode the driver reaches and maintains his desired speed without any interference from the adjacent or surrounding vehicles. Approaching: This is the mode of driving adapted by the driver when the vehicle is approaching a vehicle which is moving with slower speed. While approaching the vehicle the driver decreases the speed of the vehicle to maintain synchronization and safety distance between the two vehicles. Following: This is the mode of driving adapted by the driver following the Approaching mode, in which the driver will be following the preceding car without any conscious acceleration or deceleration. Braking: In this mode, the driver applies medium to high deceleration rates if the preceding vehicles falls below the safety distance. This can happen when the preceding vehicles stops or decreases speed abruptly. And it may be also due to another car merging suddenly between the two vehicles in front of the driver. VISSIM has a different microscopic simulation model compared to other models in terms of node-link structure. In VISSIM networks are modeled based on links and connectors. In this model, the movement of vehicles is controlled by the node-link structure, in which the vehicle after arriving to the end of link depends on the upstream or downstream node above or below to continue its trajectory (Gonzalez, 2006). The simulation model in VISSIM consists of two parts: car following model and lanechanging model. The car following model, also called as spacing-model describes the driver behavior and the movement of the vehicle. A faster moving vehicle starts to decelerate as it reaches the safety distance threshold to a slower moving vehicle. It cannot exactly determine the 10

16 speed of the preceding vehicle, but the vehicle decreases its speed until it falls below the speed of the preceding vehicle and then starts to slightly accelerate again after reaching another perception threshold. In VISSIM the function does not use a single acceleration and deceleration value but it uses the functions to represent the differences in a driver s behavior. For each vehicle class there are two acceleration and two deceleration functions, represented as graphs. They are Maximum acceleration Desired acceleration Maximum deceleration Desired deceleration These values are predefined for some vehicle type in VISSIM. These are called default vehicle types. They can be edited for each acceleration mode and for each vehicle type by changing the minimum and maximum values. Figure1 Graphs defined for the function of Maximum Acceleration The graph shown in Figure 1 consists of three different curves showing the minimum, mean and maximum values which depict the stochastic distribution of acceleration and deceleration values. The vertical axis shows the acceleration value and the horizontal axis shows 11

17 the corresponding speed. Pressing the button BEST FIT will determine the minimum and maximum values on both axes using the graph. NETWORK CODING Description of the Network The proposed project area consists of a freeway section; four major interchanges connected by frontage roads on both eastbound and west bound directions. The VISSIM micro simulation model was used for the development of the network of the corridor. The network was constructed using 2D environment and then converted into 3D mode by assigning an elevation (arbitrary) to the links and connectors to provide a 3D view to the network. The roadway network is designed in VISSIM using the details obtained from the Google Earth and then the network geometry was checked against the Sign and Striping Layout provided by the Lubbock District Department of Transportation Office and also against the geometry observed in the real field from the visits made to the project area. The network was designed on the background image, obtained from the Google Earth, which has to be scaled to the real world dimensions. The area of interest is a 5 mile portion, starting from IH-27 and extending through until the spur as shown in Figure 2. Figure 2 Google Earth Image of the Project Area 12

18 The network is developed using links and connectors. After the whole network has been modeled, the geometric design has been checked (the length of the curves, ramps, distance between the two intersections of the interchange, number of lanes, width of the lanes, etc.). After the roadway network, traffic control systems (e.g., signal, stop and yield controls etc.), and total volume inputs were coded throughout the network. The signal controller is designed as a fixed time controller for all the intersections and the signal timing data was collected for all the intersections. The Volume inputs were obtained from the raw data files provided by the TxDOT office of Lubbock District. The simulation was run with the original volume and signal timing plan. The sample snapshots of the network coding and the simulations are shown in Figure 3 and Figure 4. Figure 3 Coding of traffic control systems at University Avenue 13

19 Figure 4 Snapshot of simulation at University Avenue Network Modeling As described in the previous section, the relevant features of the test site were coded in the background image downloaded from the Google Earth. Scale was established on this image by matching the geometric details mentioned in the Sign and Striping Layout and Geopak files, provided by the Lubbock District of TxDOT. Network Geometry Links Links are used to model the road network in VISSIM. The link can have multiple lanes but not multiple sections. For a link with multiple lanes, the width of different lanes is assigned a width, derived from the geometric sources of the Loop. This allows configuring a network for the real field conditions. The link is uniform throughout its length in number of lanes and width. Wherever the network needed multiple sections, either different links are modeled or the link was split at desired locations. Each link in the network can be given a unique number, name, type and number of lanes, based on the requirement. The length of the link is displayed in the Link Data window automatically after the link was coded in the network. In addition, VISSIM allows the links to be closed for specific type of vehicles (e.g. Heavy vehicles) (PTV, 2005). 14

20 Connectors In general, connectors are used for the turning movements and to change the route of a vehicle. The number of lanes of the connector is also uniform throughout the length and depends upon the number of lanes of the two links connected through the connector. The weaving behavior of the vehicles and their tendency to change their route can be controlled by the parameters of the connector. The Emergency stop distance and lane change distance of the connector are used to fine tune the weaving behavior, on-ramp and the off-ramp flow in order to simulate the weaving behavior as real as possible. Control Devices Signal controllers All types of signal controllers in the network are defined in the signal control window. The signal controller in VISSIM can be defined to simulate a fixed time controller as well as a detector actuated controller. The list of all signal controllers that can be defined in VISSIM is o External o Fixed time o NEMA o SIEMENS VA o TRENDS o VAP (Vehicle Actuated Signal Control) o VAS o VS-PLUS For each of the signal controller a unique name and number can be assigned. For this research study, all the signals controllers in each simulation network are designed as fixed time signal controllers. The signal timings were obtained from the field observation. The signal controller and the signal timing plan of the University Avenue are shown in Figure 5. 15

21 Figure 5 Snapshotof the Signal Controller in VISSIM Stop signs STOP signs are defined in the network at intersection approaches along with priority rules wherever the geometry of the network demanded them. A STOP sign gives directions to the vehicles approaching the intersection to stop at least one time irrespective of the conflicting traffic. STOP signs are used to model the regular STOP signs as well as Only on Red STOP signs. Only on Red stop sign is a typical STOP sign which is active only when the selected Signal Controller shows Red. The STOP signs were coded in the network, to provide right turning movements on Red. Desired Speed Decisions The desired speed decisions are placed at the locations where a permanent speed change has to be made by the vehicles traveling on the network through the direction of the link. Each vehicle gets a new speed decision from the relevant speed distribution as it crosses over the desired speed decision. Only then it reacts to the new speed either by acceleration or deceleration according to its desired acceleration/deceleration function. The typical application is the location of a speed sign in reality. Other applications include entries or exits of urban areas or narrow lane widths. These are defined at the entrance of each link in the network. 16

22 Reduced Speed Decisions These signs are modeled for short sections of slow speed. The use of reduced speed areas is advantageous over the use of desired speed decisions at such situations where the speed of the vehicle should be decreased for shorter lengths. Upon arriving at a reduced speed area, each vehicle speed is decreased to the specified reduced speed decision. After passing the reduced speed area, the vehicle automatically regains its original velocity. Reduced speed areas are modeled for curves and turning movements. Thus they are placed on connectors rather than links. When the two reduced speed areas with the same properties are placed close to each other then the vehicles affected by them will continue with the reduced speed even between the two areas. This feature was used in this study to model the speed of the vehicles at curves especially turning movements. Priority Rules Generally, these rules are defined at non-signalized intersections, and locations where the links are either separated or joined. They are used to designate the right-of-way for the conflicting movements. These signs are placed on the connectors, which provide exclusive right turn movements for the vehicles and the U-turn movements for the vehicles in the network. These are modeled in the network to provide U-turn movements at the interchanges. Simulation Parameters The parameters that control the traffic simulation are accessible in SIMULATION PARAMETERS window. These parameters control the vehicle behavior in the simulation. The interaction between the vehicles is bound to change, when changing these parameters. Thus the changing of these parameters should be avoided during the simulation run. Traffic Traffic volume The traffic volume is assigned at the starting of the link. The volumes of the corresponding links at the starting point of network on Eastbound and Westbound are modeled in the network. The traffic volume between the interchanges is distributed by using the relative flow of the 17

23 traffic entering and exit the on and off ramps. If the defined volume exceeds that on the particular link, the traffic will be congested on that link until space is available. Traffic compositions Traffic composition is a mix of vehicle classes defined for a particular section of the network. A traffic composition consists of proportions of different vehicle classes, assigned for the specific portion on the network. Also pedestrian flows are to be defined as a traffic composition. Routes and Routing decisions A route is a fixed sequence of links and connectors along which the assigned traffic volume runs through the simulation. A route can have any length starting from a link or a turning movement and stretching throughout the entire network. A routing decision will have effect only on vehicles of a class that is contained in the routing decision and not having any further routing information. If a vehicle is already assigned to a route then it has to pass its destination point prior to be able to receive new routing information. This is applicable to only routing decisions but not the partial routing decisions. Partial routing decisions are defined within a routing decision to direct some portion of the traffic along different route temporarily. But the starting and ending point of the traffic remains same as the main routing decision. Others Data Collection Points Data collection points can be defined anywhere on the network (Links & Connectors). The data collection points were defined on the network at different sections and at the locations, where the actual traffic data was collected in the real field. The data collection points are defined on each lane at every section and the volume of the traffic traveling through the section is recorded at the end of simulation into a *.MES file. The format of the output file can be selected, by using the options specified in the EVALUATION FILES. The output file consists of the Data collection 18

24 point number, the number of vehicles passed through the collection point, and the time period for which the data has been collected. Traffic Data The main traffic data sets used in the simulation are field data observed by TxDOT. The research team has also conducted field observations by using the moving car method. The data are lane based and sorted by vehicle type, speed, lane number, volume and density. These data are used to validate and calibrate the simulation models. Observed traffic data The traffic volumes on the south Loop 289 was collected on both directions (Eastbound and Westbound) during the morning peak hours ( am) and the afternoon peak hours ( pm). The observations were conducted on September 12, 2007 and September 13, 2007 in the morning and the afternoon peak by using the moving car observation technique. Moving-Vehicle Technique: In this technique, observations are made by several observers moving in a vehicle around the test site. While the vehicle is moving the observer collects the relevant data of the test site on both directions. The vehicle is assumed to be moving in East and West directions. The data collected while the vehicle is making the round trip are Travel time of the vehicle in east direction (T e ), in minutes Travel time of the vehicle in west direction (T w ) in minutes Number of vehicles traveling west while vehicle is moving east (N e ) Number of vehicles overtook the testing vehicle while it is traveling in west direction (O w ) Number of vehicles overtaken by the testing vehicle while it is traveling in west direction (P w ) (N e + O w P w )60 T e +T w Then the volume (V w ) in the westbound direction can be obtained from the expression (Ne + Ow - Pw)60 Vw = Te + Tw 19

25 Table 5 Observation table of the moving car method Date and time Travel time No. of vehicles in No. of vehicles No. of vehicles (min) opposite direction overtook by testing overtaken by testing WESTBOUND vehicle vehicle Sep 12, 2007 AM Sep 12, 2007 AM Sep 12, 2007 AM Sep 12, 2007 AM Sep 12, 2007 PM Sep 12, 2007 PM Sep 12, 2007 PM Sep 12, 2007 PM EASTBOUND Sep 12, 2007 AM Sep 12, 2007 AM Sep 12, 2007 AM Sep 12, 2007 PM Sep 12, 2007 PM Sep 12, 2007 PM Sep 12, 2007 PM Date and Time Travel time No. of vehicles in No. of vehicles No. of vehicles opposite direction overtook by testing overtaken by testing WESTBOUND vehicle vehicle Sep 13, 2007 AM

26 Sep 13, 2007 AM Sep 13, 2007 AM Sep 13, 2007 AM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM EASTBOUND Sep 13, 2007 AM Sep 13, 2007 AM Sep 13, 2007 AM Sep 13, 2007 AM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Sep 13, 2007 PM Data from TxDOT The data needed to determine the LOS of the South Loop 289 and for the simulation of the traffic in VISSIM is obtained from The Lubbock District of the Texas Department of Transportation (TxDOT). Two data sets were obtained. The first data set includes detailed traffic data (speed, class, occupancy, and volume) from two observations conducted in September and October of 2007; The second data set was provided in April 2008, which contains the volume, 21

27 speed, occupancy data from three stations including Spur 327, Memphis Avenue, and Avenue P. The data was provided in two different modes: raw data files and the vehicle count from MVD s (Moving Vehicle Detector). The traffic volumes extracted from the raw data files were used to model the simulation networks and the data from the MVD s is used to recheck the speed in the simulation. Types of raw data files All of the raw data files obtained from the TxDOT can be categorized into three different file types: Axle Class and Speed (ACS) Axle Class and Volume Axle Class and Speed (ACS) The data set was subcategorized by vehicle class, lane number and speed section. In detail, these files provide the number of vehicles of each vehicle class, categorized speed on each lane. The speed was categorized into two categories, i.e. intervals observed in the obtained data were 0-50 and i.e. in mi/h. These data were obtained mainly on the mainlanes of the South Loop 289, eastbound and westbound. Axle Class (AC) This type of data file provides the volume subcategorized into vehicle types and lane number. It was observed from two locations in the network, the exit flyover from Loop 289 to I- 27 north and the entrance flyover from I-27 to Loop 289. Volume: These files provide the total number of vehicles (including all vehicle classes) per each lane. It is provided for most of the locations on the network, and the traffic data of all the ramps and service roads including the eastbound and westbound mainlanes between the Indiana Avenue and Quaker Avenue. 22

28 The traffic data were collected in two different phases, with each phase lasting for two days, the first phase was conducted on September 25 and September 26 and the second phase was conducted on October 2 and October 3, In each phase, the data were collected until a certain time on day one and was continued on day two for the remaining period of time frame. For example, the hourly volume at eastbound mainlanes at the Slide road overpass was collected until 4:00 on 9/25/07 and for the rest of the time on 9/26/07. The locations where the data were collected in each phase are detailed in the following. Figure 6 shows the locations where the data were collected in phase one. Mainlanes: 1. Eastbound mainlanes just west of I Eastbound mainlanes between Indiana Avenue and University Avenue. 3. Eastbound mainlanes between Quaker and Indiana. 4. Eastbound mainlanes on the slide road overpass. 5. Westbound mainlanes between I-27 and University. Ramps: 1. Entrance and exit ramps to the West of Slide Road. 2. Entrance and exit ramps to the East of Slide Road. 3. Westbound exit ramp to the Slide Road. 4. Westbound on ramp from Indiana. 5. Entrance and exit ramps to the East of Indiana Avenue. 6. Westbound on ramp from University Avenue. 7. Westbound exit ramp to the University Avenue. 8. Entrance ramp to the West of IH Flyover from I-27 to South loop Flyover from South loop 289 to I-27. Service Roads: 1. Westbound at the exit ramp to Slide Road. 2. Westbound service road at exit ramp to Quaker Avenue. 3. Westbound service road at on ramp from Indiana Avenue. 23

29 4. Westbound service road at exit ramp to Indiana Avenue. 5. Westbound service road at on ramp from University Avenue. 6. Westbound service road at exit ramp to University Avenue. Figure 6 Locations on the network where the data were collected in phase 1 Mainlanes: 1. Eastbound mainlanes between University Avenue and Indiana Avenue. 2. Eastbound mainlanes between Indiana Avenue and Quaker Avenue. 3. Eastbound mainlanes at the Slide road overpass. Ramps: 1. Eastbound on ramp to the West of Quaker Avenue. 2. Eastbound on ramp to the East of Quaker Avenue. 3. Eastbound exit ramp to Indiana Avenue. 4. Eastbound on ramp to the East of Indiana Avenue. 5. Eastbound on ramp to the East of University Avenue. 6. Eastbound exit ramp to US 87. Service Roads: 1. Eastbound service road at the entrance ramp to the west of Slide Road. 2. Eastbound service road at the entrance ramp to the west of Quaker Avenue. 3. Eastbound service road at the entrance ramp to the east of Quaker Avenue. 4. Eastbound service road at the exit ramp to Indiana Avenue. 24

30 5. Eastbound service road at the entrance ramp to east of Indiana Avenue. 6. Eastbound service road at the exit ramp to University Avenue. 7. Northbound road set at south of the Loop 289 on Slide Road. 8. Southbound road set at North of the Loop 289 on Slide Road. 9. Southbound road set at North of the Loop 289 on Quaker Avenue. 10. Northbound road set at south of the Loop 289 on Quaker Avenue. 11. Southbound road set at North of the Loop 289 on Indiana Avenue. 12. Northbound road set at south of the Loop 289 on Indiana Avenue. 13. Southbound road set at North of the Loop 289 on University Avenue. 14. Northbound road set at south of the Loop 289 on University Avenue. Figure 7 Locations on the network where the data were collected in phase 2 Missing and wrong data The locations where the data were missing: 1. Eastbound and westbound mainlanes just west of I-27 does not have the volume for lane 3 in the afternoon. 2. Eastbound exit ramp to the University Avenue. 3. Westbound exit ramp to the Quaker Avenue. 4. Eastbound service road at on ramp to east of the University Avenue. 25

31 The locations where the data are wrong: 1. Eastbound and westbound mainlanes between Indiana and Quaker has unrealistic volume. 2. The eastbound entrance ramp to the east of Slide road has unrealistic volume. 3. Westbound service road at Slide road exit has unrealistic volume. Sorting the data The peak hour volumes were extracted from the raw data files using a C++ program. This program takes the raw data text file as input and exports the output to a text document. The peak hour times considered in this program were 7-8am and 4-5pm.This program was applied only to the two data type files of Axle Class Speed and Axle Class, as the third type of files do not have the volume of different vehicle classes, but only the total volume including all vehicle classes. The output file exhibits the lane-based peak hour volume for both morning and afternoon, of each vehicle class based on the FHWA classification. And the output file further displays the peak hour volume with respect to speed sections of each lane for the data files of type Axle Class Speed. The third type files (Volume) were converted into Microsoft Excel Spreadsheets and the peak hour volumes for morning and afternoon were derived from the spreadsheets. Later, the afternoon peak hour was reconsidered to be 5-6pm, because it was observed that, at most of the locations the hourly volume between 5pm-6pm is more than that between 4-5pm. Hence, all the raw data files (all the three types) were converted into Microsoft Excel spreadsheets. Consequently, the morning (7-8am) and afternoon (5-6pm) peak hour volumes were derived for all the data files (all the three types) from the spreadsheets. The missing and wrong data were accounted based on the inflow and outflow of traffic volume through the ramps. But at some locations where the recovery of the data was not possible the available hourly volume of that location was used. The only location where the data cannot be recovered from TxDOT s data sets is on the eastbound mainlanes between University Avenue and I-27 for afternoon peak hour, the data from the floating car observation is used for this section. 26

32 Model Calibration and Validation Calibration of the model is to fine tune the device parameters of the model, in order to enhance its realism. Validation is the process of checking the model for any systematic errors. After all the features are modeled in the network, the model was calibrated to make the simulation replicate the actual field conditions. Some of the parameters affect the models performance on a global scale while some of them have a local effect. The parameters which were calibrated are mentioned in the following Parameter Calibration Model parameters in VISSIM can be classified as Car-following parameters Lane changing parameters Kinetic parameters Vehicle parameters Car following and the lane-changing parameters control the driving behavior of the vehicles in the model. Kinetic parameters consist of parameters which control the speed and acceleration of the vehicles (Benekohal and Chittori, 2007). Finally, vehicle parameters describe attributes associated with each vehicle type modeled. Calibration and Validation of the model generally are interrelated. Calibration of the parameters will affect the results of the validation of the model. Hence after calibration, the results of the validation are checked to assume they are in an acceptable range. If the results are not satisfactory the model has to be calibrated again to make the simulation replicate the real field conditions. The first trial of calibration showed that the distance of lane changing on the mainlanes of the network is not reasonable. The lane change distance parameter associated with the connectors is then fine tuned, based upon the length of the link and the volume of traffic exiting the freeway through the off ramp. As most of the commuters in peak hours are familiar with the driving conditions, they move over to their desired lane much earlier before reaching the posted distance. Although there 27

33 is a random variation in this perceived distance to the exit, there is no data to approximately estimate an average distance. The next objective in the calibration process was to adjust the car following parameters in the network. In VISSIM, the car following model is defined by the Wiedemann model. The Wiedemann model consists of 10 car-following (CCO-CC9) parameters which are assigned with default values. These default values were used initially in the simulation but modified later on to adjust the lane changing behavior. Generally, during the peak hour people tend to accept smaller gaps for lane-changing. The first run of the simulation model without changing the default values has indicated that the vehicles are stacked in the network in order to make a lane change, which is not the situation prevailing in the real field. Hence the CC1 parameter values are reduced to 0.85 and 0.80 (default value is 0.90) at some locations depending upon the volume of traffic and number of vehicles tending to make a lane change. The CC4 parameter is changed to 0.3 (default value 0.35) and CC4 is changed to -0.3 (default value -0.35). These changes in the car-following parameters have decreased the car-following distance and the lane changing gaps in the simulation model. Validation of the simulation model The simulation model is validated after each pass of simulation and checked for validity using two statistic tests, correlation coefficient and Root Mean Squared-Error. The two tests were conducted on the original traffic volume obtained from field observation and the volume obtained from the simulation output. Correlation Coefficient Correlation coefficient (r 2 ) indicates how closely the model predicted data matches the observed data. Its value lies between 0 and 1. A correlation coefficient value closer to 1 is desirable. The formula for the term is: r 2 = ( n i n i Volume ( Count )( Volume ) 2 i ( Count i i i i i 2 2 Volume ) )( ( i i n Count i i i Volume i 2 Count ) ) i 2 28

34 Where Count j is the observed ground count by direction for link j, Volume j is the estimated directional volume for link j, n i is the number of directional counts in the volume group i such that j = 1, 2, 3,..n i, and x i is the average directional count for volume group i. Where n is the total number of links with a count, Count i is the observed volume (by direction) on link i, and volume i is the estimated volume (by direction) on link i. The following figure shows a scatter plot between observed counts and VISSIM simulated volumes for the study network. Figure 8 The observed counts and simulation volume To enhance the realism of the simulation, the model is calibrated based on traffic volume and the relative flows of the routing decisions on the mainlanes and ramps. The overall correlation value of 0.97 is obtained, which is acceptable considering the size of the model and the data of the original counts. Table 6 Observation table for the correlation coefficient N Count Volume (Count) Count^2 Volume^2 (Volume)

35 N = 8 Σ = Σ = Σ = Σ = Σ = Root Mean Squared Error The correlation coefficient of the model is however acceptable, a high value of correlationcoefficient itself is not adequate to consider the model to be accurate. Therefore a second statistic, RMSE was used along with the correlation-coefficient. Conducting the RMSE test will reveal if the model has any systematic errors. The value of RMSE varies from 0 to 1 and a value closer to 0 is desirable. The %RMSE formula is defined as: % RMSE i = ( Count j j Volume j ) n 1 Where Count j is the observed ground count by direction for link j, Volume j is the estimated directional volume for link j, n i is the number of directional counts in the volume group i such that j = 1, 2, 3,..n i, and x i is the average directional count for volume group i. The %RMSE is calculated between the original volumes and the volumes collected from the simulation. The % Root Mean Squared Error obtained is 0.09, which is closer to zero and therefore in an acceptable range. Table 7 Observation Table of the %RMSE N Count Volume (Count) Count^2 Volume^2 (Count - (Volume) Volume)^ i x i 2 30

36 N = 8 Σ = Σ =29277 Σ = Σ = Σ = Σ = Model Modifications New network models are developed in VISSIM by adding auxiliary lanes to the basic network and also by converting the existing ramp configurations, from Diamond interchanges to X patterns and X pattern to Diamond interchange. The auxiliary lanes are added between the entrance and exit ramps along South Loop 289, both east bound and west bound. These new models will be used to evaluate and analyze the corridor. The data obtained from the Lubbock District of the Texas Department of Transportation (TxDOT) is used to document existing traffic demands and assess existing operational conditions on the main lanes and on the frontage roads along the corridor. This data will also be used in the operational evaluation of potential improvements to the corridor. New network models are developed in VISSIM by adding auxiliary lanes to the basic network and also by converting the existing ramp configurations, from Diamond interchanges to X patterns and X patterns to diamond interchanges. Addition of Auxiliary lanes Auxiliary lanes are added at each entrance and exit ramps and in different combinations. The auxiliary lanes are added between each entrance and exit ramps, east bound and west bound, except at some locations where the addition of auxiliary lane would not be possible, or not logical in the real field (e.g., Slide Road overpass and the Quaker Avenue overpass). Conversion of Ramp configuration The ramp configuration was changed to X pattern at the locations where it was diamond to provide X pattern interchanges all along the network. And another alternative network was developed by converting all the X interchanges to Diamond to provide the Diamond interchange 31

37 all along the network. The interchanges, where the ramp configurations were changed from Diamond to X are a. University Avenue and South Loop 289 b. Indiana Avenue and South Loop 289 and are shown in Figure 9. And the interchange, at which ramp configuration was changed from X to diamond is Quaker Avenue and South Loop 289 as shown in Figure 10. The X pattern at the Slide Road was not completely converted to Diamond. Only the entrance and exit ramps to the east of the Slide road were changed to entrance and exit ramps respectively. Because, Conversion of the exit ramp to Brownfield to entrance ramp is not possible. The geometry of the frontage roads was slightly changed while converting the entrance ramps to exit ramps. While converting the exit ramp to entrance ramp an additional lane is provided on the frontage road in front of the converted entrance ramp. The locations and the lengths of the ramps were not changed, but the exit ramps are converted into entrance ramps and vice versa. Conversion of X pattern to Diamond interchanges and the associated level of service analysis was conducted in compliant with the initial requirements of this project. The result of the LOS analysis has shown that converting the configuration from X pattern to Diamond types does not bring promising benefits to the study site, the remainder of this report will be focused on the three new alternatives described earlier in the Introduction Section. 32

38 Figure 9 X-Configurations at University Avenue and Indiana Avenue Figure 10 Conversion of X interchanges to Diamond at Quaker Avenue 33

39 THE VISSM SIMULATION NETWORK AND RESEARCH OBJECTIVES Introduction The LOS analysis is conducted along the corridor on the basis of morning peak data for both eastbound and southbound directions, because it is observed that the network is more congested during morning peak hour compared to afternoon peak hour. The effect of each improvement alternative on the level of service and traffic lane distribution is evaluated and the westbound direction is taken as examples to demonstrate the simulation results. The morning peak hour volumes are modeled in all the simulation networks including the basic network (BN), Alternative 1 (A1), Alternative 2 (A2), and Alternative 3 (A3). The analysis is conducted based on the current and projected traffic conditions. The traffic volumes are projected for five years at an annual incremental rate of 3%, which is an average increase rate for the Lubbock city for the past five years. The analysis is conducted at five different sections on the network shown in Figure 11. The research area consists of five major interchanges; Slide Road, Quaker Avenue, Indiana Avenue, University Avenue and IH-27. At present, Indiana Avenue and University Avenue have diamond interchanges; Slide Road has an X pattern interchange and the ramp configuration changes from X to diamond at Quaker Avenue. Figure 11 Five sections on the network where the LOS analysis is conducted 34

40 The stretch of the south Loop from Slide Road to IH-27 is a five mile portion with five closely spaced interchanges. The layout of the corridor and the construction projects currently undergoing on Loop 289 and Spur 327 all have visible impacts to the travel patterns. Travelers are most likely to use the outside lanes of south Loop 289 while approaching to their destinations to avoid frequent lane changing and weaving between two closely spaced interchanges, which results in uneven distribution of traffic on mainlanes of the network. The analysis is conducted to evaluate the potential impacts of the improvement alternatives on both level of service and traffic lane distribution. As the HCM 2000 does not provide methods for lane based capacity analysis, the LOS provided in the report is based on the overall traffic across all traffic lanes. To address the lane distribution issue, the LOS tables are supplemented by detailed simulation results that depict the effect of the alternative improvement strategies on traffic lane distribution. The analysis is conducted in two sections, first section addresses the lane distribution and density analysis of all sections under each alternative network and the second section addresses the effects of implementing each alternative on the whole network. Description of the Alternatives The original tasks of this project include addition of an outside auxiliary lane to the mainlanes of south Loop 289 between each of the entrance and exit ramps from I-27 to the Slide Road and conversion of ramp configuration from X to diamond pattern between Slide Road and Quaker Avenue, as well as conversion from diamond to X pattern at the rest of the interchanges. Based on the simulation results from the first phase of the work, three alternatives were developed, which is described in the following. Alternative1 (A1): An auxiliary lane is added to the outside mainlane between each entrance and exit ramps on both eastbound and westbound directions at Slide Road and Quaker Avenue Quaker Avenue and Indiana Avenue Indiana Avenue and University Avenue University Avenue and IH

41 Alternative 2 (A2): The ramp configuration of the basic network is changed from diamond to X pattern at Quaker Avenue Indiana Avenue University Avenue Considering that providing an X pattern interchange will increase traffic volume on the frontage road, an auxiliary lane is added on the frontage road between each exit and entrance ramp. Alternative 3 (A3): This alternative is developed by providing an auxiliary lane on the mainlanes to the alternative 2. The auxiliary lane is provided on the mainlanes over the bridges on both eastbound and westbound directions. Diamond and X pattern Interchanges The project area consists of four major interchanges; consisting of one X pattern interchange at the Slide road overpass, two diamond interchanges at the University Avenue and the Indiana Avenue and a combination of X and diamond at the Quaker Avenue. Diamond Interchange: A diamond interchange is a common type of interchange; it is generally used when a freeway crosses a minor or major road. The freeway and the road are gradeseparated. For a diamond interchange on either direction, an off-ramp diverges slightly from the freeway and runs directly across the frontage road, becoming an on-ramp that returns to the freeway in similar fashion. A typical layout of a diamond interchange is shown in Figure

42 Figure 12 Diamond pattern interchange at Indiana Avenue X pattern Interchange: An X pattern interchange is a type of interchange, which has the ramp configuration opposite to that of Diamond interchange. In the case of diamond interchanges, an exit ramp is provided while approaching the intersection and an entrance ramp following the intersection. But instead, in an X-pattern interchange entrance ramp is provided before the intersection and the exit ramp is provided after the interchange is crossed. A typical X interchange is shown in Figure 13 Figure 13 X pattern interchange at Slide Road overpass 37

43 THE ANALYSIS OF LEVEL OF SERVICE ON THE MAINLANES This section provides detailed simulation analysis of the potential impacts of the three improvement alternatives on the level of service and the traffic lane distribution of the subject area. The study is conducted section by section starting from the Slide Road overpass (section 1) to IH 27 (section 5) because each alternative strategy may result in varying effects on different sections. Only the results from the westbound traffic are used for illustrative purposes, because the changes on the eastbound as result of these improvement strategies are likely to be the same. The effects of each improvement alternative for the current and future traffic demands are presented. Current condition The current level of service on the mainlanes of the corridor is first evaluated on the basis of the current traffic demand. The simulation analysis is conducted section by section from the Slide Road overpass to IH27 to the east of south Loop 289. Simulation results reveal that the corridor is relatively more congested in the morning peak than in the afternoon peak and the level of service ranges from B to D for both eastbound and westbound. The simulation results are presented graphically for each section. For illustrative purpose, the effects of the improvement alternatives on the westbound of corridor are presented in this section. The current traffic condition in terms of total and lane by lane volume during the morning peak on the westbound is illustrated in Figure 14. The values on the Y-axis represent the traffic volume in vehicle per hour, while the labels on the X-axis represent the five sections of the network as follows: Slide Road overpass (S1) Slide Road & Quaker Avenue (S2) Quaker Avenue & Indiana Avenue (S3) Indiana Avenue & University Avenue (S4) University Avenue & IH-27 (S5). 38

44 Figure 14 Lane distribution of current traffic on the basic network (westbound) The section by section hourly volume, as depicted in the figure, is 4187 veh/hr for section 5, 3995 for section 3, 3973 for section 4, 2730 for section 1, and 1848 for section 2 in a descending order. The corresponding section by section density is then calculated based on the hourly volume and the occupancy data, which is shown in Figure 15. Similarly, the values on the X-axis represent each section of the network and the values on the Y-axis represent the density of each section on mainlanes in vehicle per mile. Figure 15 Traffic Density of basic network for current traffic (westbound) 39

45 So, the LOS of the existing network under current traffic condition can be determined, (Table 1), which ranges from B to D along the corridor. The sections with lowest LOS are sections 3, 4, and 5, all in the category of D. Section 1 and 2 are in LOS B in the westbound and LOS C and B, respectively in the eastbound. It can also be visulized from Figure 14 that the vehicles are not evenly distributed on the mainlanes, especially in sections 1 and 2 where traffic is less congested. The lane by lane traffic volume is shown in different colors in the figure. From the bottom to top, it represents the inside/median lane, the middle lane, and the outside lane. For instance, the total volume in section 1 is 2730 veh/hr, which is composed of 641 veh/hr on the inside lane, 739 veh/hr on the middle lane, and 1350 veh/hr on the outside lane. The unevenly distributed pattern is a result of many issues including the relatively low traffic volume in section 1 and 2, and the high percentage of short distance travellers. Section by section evaluation of the improvement alternatives The proposed improvement strategies have varying impacts on different segments of the corridor. For instance, changing the ramp configuration from diamond to the X pattern would alleviate the traffic on the mainlanes but at the same time increase the volume on frontage roads. Adding an auxiliary lane to the mainlanes, on the other hand, would certainly improve the level of service on the mainlanes due to increased capacity but may also result in more unevenly distributed traffic at some places. This section presents the effects of the three improvements strategies on each segment of the corridor. The effects of different alternatives on section 1: the Slide Road overpass The simulation results reflecting the effects of Alternative 1 (A1), Alternative 2 (A2), and Alternative 3 (A3) on traffic volume and lane distribution along the Slide Road overpass (S1) is shown in Figure 16. Note that A1 and A3 have four segments because both of these two alternatives provide an additional lane on the overpass. 40

46 Figure 16 Traffic volume and lane distribution at the Slide Road overpass as result of different alternatives (westbound) The labels on the X-axis represent the alternative networks and each column on the X-axis shows the number of vehicles traveling on that specific lane in case of each alternative. For example, the column corresponding to label A1 gives the total hourly volume at the given section as a result of Alternative 1, as well as the composition of the traffic across each individual lane. It can be seen that the number of vehicles on the basic network and Alternative 1 is almost the same because there is no change of ramp configurations involved in A1. The volume is considerably increased in the case of Alternative 2 (A2) as the ramp configuration is changed from diamond to X pattern at the upstream interchange (Quaker Avenue). This change in ramp configuration has provided an entrance ramp on to the mainlanes instead of an exit ramp (at the section between Slide Road and Quaker Ave). This has led to an increased volume on the mainlanes at this section. It is discussed earlier that providing an X pattern interchange may increase the volume on the frontage road because short trip travelers may choose not to use the mainlanes. But, when the volume on the frontage road is increased the vehicles are encouraged to pass on to the entrance ramp when an auxiliary lane is provided on the mainlanes between the entrance and exit ramps. Hence, the volume for the Alternative 3 is slightly higher compared to Alternative 2. 41

47 Since all these three strategies have resulted in, to varying degrees, better lane change distributions as can be seen from the same figure, the judgment is left to their effects on the level of service. The chart shown in Figure 17 illustrates the changes in density at the Slide Road overpass as a result of different improvemet stratgies. As the graph shows, the Alternative 1 (A1) reduces the denstiy at the Slide Road overpass by almost 25% due to the addtion of an auxiliary lane on the bridge. This density still falls into the category of B in terms of level of service. It is also observed that the Alternative 2 (A2) increases the density at this section due to the converstion of the interchange pattern from diamond to X. This change in density leads to one grade down (from B to C ) in terms of level of service. The result from Alternative 3 (A3) is somewhat not expected but still easy to understand. As A3 involves two major changes, including conversion of ramp configuration between Slide Road and Quaker Avenue from diamond to X pattern and addition of an auxiliary lane at the Slide Road overpass. Though this has increased the traffic volume on the bridge, it does not have significant impact on traffic density due to the increased capacity brought by the additional lane. As a result, the density is almost the same as that of the basic network. Figure 17 Traffic density at the Slide Road Overpass as result of different alternatives (westbound) 42

48 In summary, the Alternative 1, i.e., adding an auxiliary lane on the mainlanes between each entrance and exit ramp turns out to be the most effective strategy to reduce traffic density and improve the level of service at Section 1. However, this strategy may not be needed at current time as the current LOS is relatively low at this section. The Alternative 2 (A2) increases the density on mainlanes due to the extra volume brought by the entrance ramp, but it also alleviates the pressure on the intersection by getting the traffic onto the bridge. So, the effectiveness of A2 needs to be examined in conjunction with detailed evaluation of the frontage road and the intersection. The Alternative 3 (A3) maintains the density level at 18 veh/mi as a result of a combined effect of the change in ramp configuration and addition of the auxiliary lane, its effect also needs to be examined in association with detailed evaluation of the frontage road and the intersection. Effects of the alternatives on Section 2: Slide Road & Quaker Avenue Figure 18 shows the changes in traffic volume and lane distribution on the mainlanes at the section 2 (i.e., the section between Slide Road and Quaker Ave) as result of different improvement alternatives. Note that there are only three mainlanes in this section because the additional lane is added to the frontage road. It can be visualized that the traffic volume in the case of the Alternative 1 is almost the same as that in the basic network (1828 veh/hr and 1845veh/hr). Alternative 2 has resulted in a significant increase in traffic volume (2872 veh/hr) on the mainlanes, due to the change in ramp configuration at the Quaker avenue. Also can be observed from the simulation result is the slight increase in traffic volume as result of Alternative 3, which is due to the auxiliary lane on the bridge over the Quaker Avenue. 43

49 Figure 18 Traffic volume and lane distribution at the section of Slide Road and Quaker Ave as result of different alternatives (westbound) None of these three improvement alternatives has resulted in significant changes in lane distribution patterns. Though both Alternative 2 and Alternative 3 change ramp configuration from diamond to X pattern, the effect on lane distribution is not visible due to the relative low v/c ratio at this section. The resulted LOS from the Alternative 2 is B, while the Alternative 3 results in a LOS of C. This simply confirms the fact that traffic will be more evenly distributed only if there are more vehicles on the mainlanes. Figure 19 Traffic density at the section of Slide Road and Quaker Ave as result of different alternatives (westbound) 44

50 Figure 19 depicts the changes in traffic density brought by different alternatives at the segment of Slide Road & Quaker Avenue. It can be observed from this chart that traffic density from Alternative 1 is almost the same as that from the basic network (BN). This further confirms the validity of the simulation model because there are no modifications made to the geometry of the network at this section in case of Alternative 1. The traffic density is increased in the cases of Alternative 2 and Alternative 3 due to the changes in ramp configuration at the adjancent segments. It is clear that Alternative 2 and Alternative 3 will result in higher traffic volumes on the mainlanes at this section. This, however, does not necessarily mean that they are not effective. Both of the strategies will significantly reduce the traffic density on the frontage roads, due to the diversion of vehicles from frontage road to the freeway and the addtition of the auxilliary lane on the frontage road. Effects of the alternatives on Section 3: Quaker Avenue & Indiana Avenue The section between Quaker Avenue and Indiana Avenue (Section 3) has a level of service D in both westbound and eastbound directions under current traffic conditions. As a result, the vehicles are more evenly distributed in this segment than in section 1 and section 2, as shown in Figure 14. Hence, the performance of the alternative strategies at this section can be examined primarily based upon their effectiveness in improving the level of service of the subject area. It can be seen from Figure 20, both Alternative 2 and Alternative 3 will significantly reduce traffic volumes on the mainlanes. For instance, the total traffic volume on the mainlanes of the basic network is 3321 veh/hr (1228, 1435, and 1658 veh/hr) under current conditions. This is brought down to 2433 veh/hr (434, 1055, and 944 veh/hr), an almost 27% decrease by simply converting the ramp configuration from diamond to X pattern, as in the case of Alternative 2. The traffic volume brought by Alternative 3 is slightly higher compared to that from Alternative 2, due to the additional auxiliary lane added on the bridge upstream of the section. 45

51 Figure 20 Traffic volume and lane distribution at the section Quaker Ave and Indiana Ave as result of different alternatives (westbound) The effect of A1, A2, and A3 on traffic density at this section is shown in Figure 21, from which one can observe that changing ramp configuration at the adjacent interchanges has resulted in considerable reduction in traffic density (A2 and A3). Adding an auxiliary lane has also reduced the density to a significant extent as in the case of A1, however, this is mainly due to the increase of number of lanes on the freeway. Also notable is the slight increase in traffic density as a result of Alternative 3 compared to Alternative 2. This is because the changes in ramp configuration will increase traffic volumes on the frontage roads. Meanwhile, as the traffic on the frontage road increases, the vehicles on the frontage road tend to move on to the auxiliary lane provided on the bridges through the entrance ramp. 46

52 Figure 21 Traffic density at the section of Quaker Ave and Indiana Ave as result of different alternatives (westbound) It is obvious that Alternative 2 and Alternative 3 outperform the Alternative 1 in terms of improving the level of service at this section. As Alternative 3 involves addion of an auxiliary lanes on the bridges, which increases the construction cost, it can be concluded that Alternative 2 is the best option for the section of Quaker Avenue and Indiana Avenue. As the conversion of ramp configuration from diamond to X pattern will increase the traffic volumes on the frontage road as well as the flow pattern at the intersections, detailed LOS analysis of the frontage road and the intersections is necessary. Effects of the alternatives on Section 4: Indiana Avenue & University Avenue The changes made to the mainlanes and the frontage roads between the Indiana Avenue and University Avenue are identical to that made to Section 3. The resulted impacts from these three alternatives, as shown in Figure 22 are therefore similar. The number of vehicles on mainlanes for the basic network and the Alternative 1 are higher compared to the other alternatives, as in the case of Section 3. Similarly, this change in vehicle volume is mainly due to the change in ramp configuration at Quaker Avenue and Indiana Avenue. As discussed earlier, the X pattern interchange will result in reduced number of vehicles 47

53 on the mainlanes between these two interchanges. Hence the traffic volumes as result of the Alternatives 2 and Alternative 3 are significantly less than that of the basic network and Alternative 1. Figure 22 Traffic volume and lane distribution at the section of Indiana Ave and University Ave as result of different alternatives (westbound) The pattern of traffic lane distribution at this section is also very similar to that of the previous section. Figure 23 shows the traffic density between Indiana Avenue and University Avenue as result of different alternatives. The graph is almost identical to the result at the section of Quaker Avenue and Indiana Avenue. Hence, same conclusion can be made with regard to the effectiveness of the improvement alternatives. The Alternative 2 is the best option for this section. 48

54 Figure 23 Traffic density at the section of Indiana Ave and Universtiy Ave as result of different alternatives (westbound) Effects of the alternatives on Section 5: University Avenue & IH27 This is another section with highest v/c ratio during the morning peak, especially on the westbound. The westbound and eastbound LOS is D and C respectively under prevailing traffic and roadway conditions. All westbound traffics enter the project area from this section and most of the traffic volumes are destined towards one of the interchanges in the loop. The vehicles are more evenly distributed across the mainlanes at this section due in part to the higher traffic demand and in part to the relatively longer travel distances to the destinations from this section. The total mainlaine volume, as a result of Alternative 1, is almost the same as the basic network but across four lanes (the number of vehicles on basic network and Alternative 1 are 4187 and 4191 veh/hr respectively). Both Alternative 2 and Alternative 3 reduce the mainlane traffic volume significantly by providing the X type interchange at the University Avenue (3426 and 3418 veh/hr respectively). It can also be observed that the decrease in traffic volume in case of Alternative 2 is not that significant when compared to previous sections. Because the traffic volume entering the south Loop at IH-27 and the traffic volume exiting through the exit ramp at University Avenue are considerably less compared to other sections (in case of the basic network). 49

55 Figure 24 Traffic volume and lane distribution at the section of University Avenue and IH27 as result of different alternatives (westbound) According to the density chart illustrated by Figure 25, each alternative will improve the level of service by one grade, from D to C for the current traffic conditions. Figure 25 Traffic density at the section of University Avenue and IH-27 as result of different alternatives (westbound) One of the notable differences in the density chart at this section compared to the previous section is that the densities corresponding to Alternatives 2 and Alternative 3 are relatively 50

56 higher compared to that of Alternative 1. This is mainly because of two reasons. First, most of the traffic entering this section is from outside of the loop system, which is independent of the ramp configuration. Secondly, the traffic volume entering the study area through the entrance ramp at IH-27 and exiting from the exit ramp at University Avenue is significantly less (for basic network). Therefore, the number of vehicles a X pattern interchange can reduce on the mainlanes is also decreased. In conclusion, Alternative 1 outperforms Alternative 2 and Alternative 3 at this section because it brings lowest traffic denstiy. Summary of LOS for current condition The determination of level of service on mainlanes of south Loop 289 for the basic network and the three improvement alternatives is presented in this chapter by using the results from the westbound direction. Similar analysis was conducted on the eastbound of the corridor. The impacts of the improvement strategies on the level of service of the corridor, both eastbound and westbound, is summarized in this section. Figure 26 Traffic Density of basic network for current traffic (eastbound) 51

57 Figure 26 illlustrates the section by section traffic density on the eastbound during the morning peak and under current traffic and roadway conditions. The density chart shows that the v/c ratio or traffic density at section 2 (between Slide Road and Quaker Avenue) is relatively low compared to the other sections. The density at section 3 (between Quaker Avenue and Indiana Avenue) has increased due to the series of entrance ramps. The current level of service on both eastbound and westbound mainlanes during the morning peak under existing traffic and roadway conditions is summarized in Table 8. Adding an auxiliary lane on south Loop 289 between each on and off ramps, as in the case of Alternative 1, improves the level of service at the following sections as shown in Table 9: the section between Quaker Avenue and Indiana Avenue, both eastbound and westbound; the section between Indiana Avenue and University Avenue, eastbound; and the section between University Avenue and IH-27, westbound. Table 8 LOS of the basic network for current traffic volume SECTION Basic Network Slide Road Overpass Westbound B Eastbound C Slide Road & Quaker Westbound B Ave. Eastbound B Quaker & Indiana Westbound D Eastbound D Indiana & University Westbound C Eastbound D University & I-27 Westbound D Eastbound C 52

58 Table 9 Comparison of LOS between Basic Network and Alternative 1 Basic SECTION Network Alternative 1 Slide Road Overpass Westbound B B Eastbound C C Slide Road & Quaker Westbound B B Ave. Eastbound B B Quaker & Indiana Westbound D B Eastbound D B Indiana & University Westbound C C Eastbound D B University & I-27 Westbound D C Eastbound C C The Alternative 2 converts the ramp configuration from diamond to X pattern at each interchange of the study area and adds an additional lane on the frontage roads. It reduces the mainlane traffic by shifting the vehicles, especially those with short origin-destination trips, from the freeway to the frontage road. The effect of Alternative 2 in terms of changes in level of service is illustrated in Table 10. Table 10 Comparison of LOS between Basic Network, Alternative 1 and Alternative 2 Basic SECTION Network Alternative 1 Alternative 2 Slide Road Westbound B B C Overpass Eastbound C C C Slide Road & Westbound B B B Quaker Ave. Eastbound B B B Quaker & Westbound D B B Indiana Eastbound D B B Indiana & Westbound C C C University Eastbound D B B University & Westbound D C C I-27 Eastbound C C C 53

59 The result shows that Alternative 2 yields the LOS almost as same as the Alternative 1. The only section at which the LOS from Alternative 2 is higher than that from Alternative 1 is at the Slide Road overpass. Because in Alternative 2 the ramp configuration to the east of Quaker Avenue is changed from exit ramp to entrance ramp. Hence, the volume on the mainlanes is increased at the downstream sections. Although the LOS at the section between Slide Road & Quaker Avenue remains at the same level, the density at this section is increased (12.1 vs veh/mi) from that of Alternative 1. The Alternative 3 converts ramp configuration from diamond to X pattern at the interchanges and adds an additional lane on the bridges to accommodate the increased volume on the overpasses. From Table 11, it can be observed that the level of service is also improved compared to the basic network. However, the result is not as promising as that from Alternative 1 and Alternative 2. This is because the increased volume on the frontage road, as a result of the change in ramp configuration, tend to move some vehicles back to the mainlanes when an additional lane is provided. Table 11 Comparison of LOS between Basic Network and all alternatives SECTION Basic Network Alternative 1 Alternative 2 Alternative 3 Slide Road Westbound B B C B Overpass Eastbound C C C C Slide Road & Westbound B B B C Quaker Ave. Eastbound B B B C Quaker & Westbound D B B C Indiana Eastbound D B B B Indiana & Westbound C C C C University Eastbound D B B B University & I- Westbound D C C C 27 Eastbound C C C C Adding an auxiliary lane improves the overall LOS of the network, but the lane distribution pattern needs to be considered before implementing this alternative into the field. This uneven distribution of traffic is in part due to the high percentage of short trip vehicles on the corridor. 54

60 An alternative which can reduce this volume of traffic on mainlanes and transfer them to the adjoining local arterial street might become the best solution for this problem. Although the current alternative provides an LOS greater than the Alternative 1 at one section, the lane distribution factor makes this alternative a better solution than the Alternative 1. The lane distribution of traffic flow is almost uniform in this case, because providing an X interchange instead of diamond pattern will reduce the number of short trip vehicles on the mainlanes. All these vehicles are directed on to the local arterial street or the frontage road. This may increase the traffic flow on the frontage road and can cause congestion. But, providing an X interchange will also reduce the congestion on the frontage road because of the longer bay area. Moreover, this alternative includes an additional lane on the frontage road between each entrance and exit ramp. Hence it can be concluded that Alternative 2 is better than Alternative 1. The LOS analysis for the projected traffic The traffic projection is conducted on the basis of 3% annual increase for five years on the mainlanes of south Loop 289. The potential effects of the proposed improvement alternatives on level of service and traffic lane distribution is analyzed in a way similar to the study for current conditions. The simulation results are presented in this section. Basic Network The analysis is first conducted on the basic network without any modifications in roadway geometry. As illustrated in Figure 27, the simulation results show that although traffic volumes will increase along the corridor, there are no significant changes in terms of the level of service patterns. For instance, traffic volumes on the sections 3, 4, 5 are still more than that on section 1 and section 2, as in the case of current traffic demands. 55

61 Figure 27 Traffic volume and lane distribution on basic network under current and forecasted traffic conditions (westbound) From the density chart shown in Figure 28, it can be stated that traffic density at each section may increase for the future traffic demands, but the extent will not lead to significant changes in level of service. For instance, the LOS will be degraded to only one level down at three sections on the westbound: the Slide Road overpass, Slide Road and Quaker Avenue, and the section between University Avenue and IH-27. Figure 28 Traffic density on the basic network for current and forecasted traffic volumes (westbound) The level of service under the forecasted traffic demand for five years on the mainlanes of south Loop 289, both eastbound and westbound, is summarized in Table

62 Table 12 LOS of Basic Network for Current and Future Traffic Volumes SECTION Current Future Slide Road Overpass Westbound B C Eastbound C C Slide Road & Quaker Westbound B B Ave. Eastbound B C Quaker & Indiana Indiana & University University & I-27 Westbound D D Eastbound D D Westbound C C Eastbound D D Westbound D D Eastbound C D The LOS table shows that if no changes are made to the study area, the level of service will be degraded at some places, but not to a significant extent. Most of the places will be able to remain at the same LOS level but in higher traffic density ranges. For instance, the future LOS on westbound of Section 5, i.e., the section between University and Interstate 27 is at the edge of LOS D and LOS E with the density level of 30 veh/mi. It is worthy of note that the increases in traffic volumes will have significant impact on the frontage roads, especially at the intersections. Though detailed analysis was not conducted with regard to the level of service on frontage roads, it was observed from simulation that vehicles on the middle and outside lanes are frequently stacked due to the queues spilled back from the downstream intersections. This phenomenon is especially severe at the intersections of University Avenue and Indiana Avenue. The Alternative 1 All the three alternative improvement networks are modeled with regard to the projected traffic condition and the simulation results are compared with that from the current traffic demand. For instance, Figure 29 shows the effects of Alternative 1 on traffic volume and lane distribution with current and projected traffic data. 57

63 Figure 29 Effect of Alternative 1 on traffic volume and lane distribution with current and projected traffic data (westbound) Figure 30 depicts the effect of Alternative 1, i.e., adding an auxilliary lane on the outside mainlanes of south Loop 289 between each on and off ramp, on traffic density with current and projected traffic data. As can be seen from both Figure 29 and Figure 30, there are no significant changes from the current situation to future situation. Figure 30 Effect of Alternative 1 on traffic volume and lane distribution with current and projected traffic data (westbound) The level of service on both eastbound and southbound as a result of Alternative 1 is summarized in Table 13. As can be seen, the LOS will be downgraded at three sections as a result of the increased traffic volumes. At the section of Quaker Avenue and Indiana Avenue, the LOS is changed from B to C, for both eastbound and westbound. On the westbound of Indiana Avenue and University Avenue, 58

64 the LOS is degraded to C from B. Based on the simulation results, similar conclusions can be made that there won t be considerable changes in level of service in five years. What needs to be noted, however, is the impact of the increased traffic volume on the frontage road. Similar situation was observed from simulation in which queue spills back from the downstream intersection and extends to freeway entrance and exit ramps. Table 13 LOS of the Alternative1 for the current and future traffic demands SECTION Current Future Slide Road Overpass Westbound B B Eastbound C C Slide Road & Quaker Westbound B B Ave. Eastbound B B Quaker & Indiana Indiana & University University & I-27 Westbound B C Eastbound B C Westbound C C Eastbound B C Westbound C C Eastbound C C The Alternative 2 The Alternative 2 changes the flow pattern on the mainlanes and frontage roads by converting ramp configuration from diamond to X pattern. As described earlier, this strategy improves the level of service by diverting short trip vehicles from mainlanes to the frontage road. Similarly, Figure 31 illustrates that the increase in traffic demand in five years will not cause considerable changes in level of service on the westbound of the corridor. 59

65 Figure 31 Effect of Alternative 2 on traffic volume and lane distribution with current and projected traffic data (westbound) The change in density for both current and projected trafic demand at each section is shown in Figure 32. Figure 32 Effect of Alternative 2 on traffic volume and lane distribution with current and projected traffic data (westbound) Accordingly, the level of service for current and future traffc demands on the westbound of the corridor is shown in Table 14. Both Figure 32 and Table 14 illustrate that the changes due to the increased traffic volume are limited. 60

66 Table 14 LOS of the Alternative 2 for the current and future traffic demands SECTION Current Future Slide Road Overpass Westbound C C Eastbound C C Slide Road & Quaker Westbound B B Ave. Eastbound B B Quaker & Indiana Indiana & University University & I-27 Westbound B B Eastbound B B Westbound C C Eastbound B B Westbound C C Eastbound C C The Alternative 3 The Alternative 3 features both ramp configuration change and addition of auxiliary lanes on the bridges. This strategy provides better lane distribution than the other two alternatives for both current and future traffic situation. This feature can be visulized by Figure 33. Figure 33 Effect of Alternative 3 on traffic volume and lane distribution with current and projected traffic data (westbound) In terms of the effects on traffic density brought by Alternative 3, the difference between the current and the forecasted traffic demand is very limited, which can be observed from the density values illustrated in Figure

67 Figure 34 Effect of Alternative 4 on traffic volume and lane distribution with current and projected traffic data (westbound) The level of service under current and future traffc demands on the westbound of the corridor is shown in Table 15. Similarly, the changes in traffic demands won t have significant impacts on the performance of Alternative 3 in five years. Table 15 LOS of the Alternative 3 for the current and future traffic demands. SECTION Current Future Slide Road Overpass Westbound B C Eastbound C C Slide Road & Quaker Westbound C C Ave. Eastbound C C Quaker & Indiana Indiana & University University & I-27 Westbound C C Eastbound B C Westbound C C Eastbound B C Westbound C C Eastbound C C Even though the Alternative 3 yield similar results to that from Alternative 2, it is worthy of note that it requires more geometric modifications and thus less cost effective than Alternative 2. 62