MOJ MOJCE

Civil Engineering
Research Article
Volume 1 Issue 1 - 2016
Statistical Evaluation of Ramp Metering for a Dual Free way Corridor
Sherif Ishak1*, Syndney Jenkins2, Yan Qi3, Julius Codjoe4 and Osama A Osman1
1Department of Civil and Environmental Engineering, Louisiana State University, USA
2Kimley-Horn Consulting, USA
3Department of Civil Engineering, Southern Illinois University, USA
4Louisiana Transportation Research Center, USA
Received: September 06, 2016| Published: September 28, 2016
*Corresponding author: Sherif Ishak, Louisiana State University, 3660G Louisiana 70803, USA, Tel: 225-578-4467; Email:
Citation: Ishak S, Jenkins S, Yan Q, Codjoe J, Osman OA (2016) Statistical Evaluation of Ramp Metering for a Dual Free way Corridor. MOJ Civil Eng 1(1): 00003. DOI: 10.15406/mojce.2016.01.00003

Abstract

This study evaluates the effectiveness of ramp metering on two corridors of I-10 and I-12 in Baton Rouge, Louisiana. This is achieved by simulating both corridors with and without ramp metering. Geometric and traffic data were collected to build the network in the simulation model (VISSIM). Simulation results for travel times and delays from 25 runs were obtained for two simulation scenarios, one with and one without ramp meters. The simulation results were then analyzed statistically to investigate the impact of ramp meters on the corridors operational conditions. The comparative evaluation showed a statistically significant improvement in the corridor travel times and delays with ramp meters. Based on the simulation results, the study endorses the use of ramp metering as a successful control strategy.

Keywords: Ramp metering; Microscopic simulation; VISSIM; Freeway operation; Integrated corridor management

Abbreviations

ICM: Integrated Corridor Management; ITS: Intelligent Transportation Systems; HERO: Heuristic Ramp Metering Coordination; SZM: Stratified Zone Ramp Metering; ARMS: Advanced Real-time Metering System; CRPC: Capital Region Planning Commission; VAP: Vehicle Actuated Programming

Introduction

Traffic congestion continues to escalate and spread over the surface transportation network in the U.S. Symptoms are often observed in a number of large and medium size cities across the country as travel demand continues to exceed the existing network capacity. Conventional approaches relying primarily on capacity expansion are high cost solutions that cannot meet the rising demand and limited rights of way needed for roadway widening. In the last two decades, transportation professionals recognized the need for better management of the existing network capacity as a viable alternative to capacity expansion projects. Transportation corridors may still have unused capacity on parallel routes that can be leveraged to alleviate congestion on freeways. Such concept has been referred to as Integrated Corridor Management (ICM) and successfully applied to major metropolitan areas such as Dallas, Texas, Houston, Texas, Minneapolis, Minnesota, Oakland, California, Seattle, Washington, to name a few. The city of Baton Rouge, the state capital of Louisiana, continues to grow in population and in travel demand at an alarming rate, causing severe congestion to spread over freeways and major arterials. The existing capacity of the roadway infra-structure in the city cannot sustain the rising demand, and therefore, congestion is now frequently observed over the freeway segments of I-10 and I-12, as well as the main arterials. Such congestion may be alleviated by applying integrated corridor management strategies such as ramp metering, which is the focus of this re-search study.

Under the ICM umbrella, the operation of freeways and arterials should be optimized for various functions such as traffic incident management, work zone management, planned specie vents management, and re-current day-to-day conditions. This goal ensures more sustainable and resilient transportation network under both normal and extreme (such as emergency evacuation) operating conditions. It is possible to develop an efficient integrated corridor management by developing a ramp-metering strategy, information dissemination strategy and other ITS strategies along congested corridors.

Ramp metering aims to improve the traffic conditions by regulating the inflow from the on-ramps to the free-way main stream. For fixed time metering strategies, ramp meter timings are adjusted for different time periods during the day, and therefore, do not offer the flexibility to adapt to changing traffic conditions. Traffic-responsive ramp metering strategies, on the other hand, are based on real-time measurements from sensors installed in the freeway network and can be classified as local or coordinated. Local control is a process of selecting ramp metering rates based solely on conditions present at an individual ramp, while coordinated control is a process of selecting metering rates based on conditions throughout the entire metered corridor.

Local ramp metering strategies

While numerous studies addressed various local ramp metering strategies in the open literature, this section briefly introduces the concept through a few selected studies. Masher et al. [1]. Developed a demand-capacity ramp metering algorithm, which is a traffic responsive algorithm that measures the downstream occupancy. Papageorgiou et al. ‎[2]. Proposed a local responsive feedback ramp metering strategy (ALINEA), this had multiple successful field applications (Paris, Amsterdam, Glasgow, Munich). In another paper Smaragdis et al. [3]. Presented several modifications and extensions of ALINEA. A zone algorithm was reported to be used at Minnesota [4]. This algorithm defines directional freeway facility “metering zones” with zones having variable lengths of three to six miles. Its basic concept of the algorithm is to balance the volume of traffic en-tering and leaving each zone Ghods et al. [5]. Proposed an adaptive genetic fuzzy control approach to reduce peak hour congestion, along with speed limit control. Ozbay et al. [6]. Developed an isolated feedback based ramp metering strategy that takes into account the ramp queue. In addition to the regulation of ramp input, the strategy calls for regulation of ramp queues by explicitly incorporating them into the model.

Coordinated ramp metering strategies

The bottleneck metering algorithm is a system of ramp control, which includes several internal adjustments of volume reduction, based on downstream bottlenecks and localized adjustments such as queue over-ride [7]. ARMS (Advanced Real-time Metering System) consists of three operational control levels within a single algorithm: free-flow control, congestion prediction, and congestion resolution [8]. Wei et al. [9]. Developed a coordinated metering algorithm using artificial neural networks. Gettman et al. [10]. Presented a multi-objective integrated large-scale optimized ramp metering system for freeway traffic management, seeking to address the interaction of the freeway system with the adjacent surface-street system by providing a method to trade-off queue growth at individual ramps in a freeway corridor. Zhang et al. [11]. Developed a new freeway ramp control objective- minimizing total weighted (perceived) travel time by balancing efficiency and equity of ramp meters, compared to a previous metering objective, which minimizes the total absolute travel time. A ramp metering algorithm incorporating “fuzzy logic” decision support was developed at the University of Washington for a number of years ‎[12]. The algorithm, based on fuzzy set theory, is designed to overcome some of the limitations of existing conventional ramp metering systems. A freeway traffic control system has been in place on the Hanshin Expressway near Kobe, Japan, based on the Hanshin algorithm ‎[13]. The linear algorithm maximizes the weighted sum of ramp flows.

Another coordinated ramp metering strategies, METALINE, is a coordinated generalization (using lists of multiple values, or columnar vectors, in place of single values) of ALINEA [14]. The metering rate of each ramp is computed based on the change in measured occupancy of each freeway segment and the deviation of occupancy from critical occupancy for each segment that has a controlled on-ramp. Chang et al. ‎[15]. Proposed a metering model for non-recurrent congestion. This algorithm uses a two-segment linear flow density model. As the successor of the zone metering algorithm, the Stratified Zone Ramp Metering (SZM) Strategy has been developed and deployed in the Minneapolis/Saint Paul area [16].The SZM strategy aims to maximize freeway throughput while keeping ramp waiting times below a predetermined threshold. In a recent study Papamichail et al. [17]. Developed a traffic- response feedback control strategy, HERO (Heuristic Ramp Metering Coordination) to coordinate local ramp metering actions in freeway networks Wang et al. [18]. Proposed an area-wide ramp metering system to improve the coordination of ramp meters for system-wide optimization and on-ramp overflow minimization. In summary, coordinated ramp metering strategies have been suggested as more effective measures than local ramp metering when there are multiple congestion bottlenecks on the freeway, excessive ramp delays, and when the optimization of freeway and on-ramp performances requires the metering of several ramps.

Study Objectives

This study applies ramp metering strategies on the two corridors of I-10 and I-12 within the city of Baton Rouge in order to determine their effectiveness. This is achieved by simulating both corridors with and without ramp metering at the microscopic level using the forecasted traffic demand in the year 2012.The specific objectives of this research are to: (1) review the state of the practice of ramp metering strategies and their application in other metropolitan areas in order to learn from similar experiences and identify the various strategies used thus far, as well as their points of strengths and weaknesses, (2) identify the data requirements for developing a simulation model for the two corridors of I-10 and I-12 in Baton Rouge and estimate the forecasted travel demand is used for the year 2012, (3) select a microscopic simulation platform (VISSIM) and build the simulation network for the study area, and (4) evaluate the effectiveness of ramp metering by comparing selected network performance measures with and without the implementation of ramp meters. It is anticipated that meeting these objectives will lay a foundation for the application and implementation of ICM strategies to reduce congestion on the freeway and arterial systems in Baton Rouge.

Data Collection

Traffic data was collected from the Capital Region Planning Commission (CRPC) to reflect the forecasted origins and destinations for all on and off ramps along the 7 mile corridor of I-10 and the 4 mile corridor of I-12in Baton Rouge, Louisiana; see Figure 1. The two corridors currently experience heavy recurrent congestion during the morning and evening peak periods. Geometric data was collected to build the study area network in the simulation model. The planning-level network was provided in the form of a Trans CAD file, based on the Baton Rouge Metropolitan Transportation Plan Update of December 2007.The morning interval was defined from 6:30-9:30 AM and the evening interval from 3:30-6:00 PM. With this data, a Friction Factor Matrix was created in order to determine the origin-destination flows for the morning peak period only between on- and off-ramps as origins and destinations, respectively. The gravity model was then applied to synthesize an Origin-Destination (O/D) matrix based on the estimated friction factor matrix. Table 1 shows the non-zero values of the friction factor matrix for the 25 origins (on-ramps) and 25 destinations (off-ramps), as well as the corresponding values of the O/D matrix after the first iteration and the final O/D matrix. The friction factors were inversely proportional to the distances between the origins and destinations along both corridors. A total of 14 iterations were required to reduce all errors in the attractions to 1% or less. The final O/D matrix was then used in the simulation model to predict the network performance with and without ramp metering strategies in the target year.

Figure 1: Study Network for I-10 and I-12; Baton Rouge, LA.
Table 1: Friction Factor Matrix, First Iteration and Final Origin/Destination Matrix.

Methodology

Network description

VISSIM is a behavior based microscopic simulation model that was adopted in this study. The freeway corridors of I-10 and I-12 were coded in VISSIM using links and nodes. Figure 2 shows a snapshot of both corridors as coded in VISSIM. Routes were created from every specific entrance point (on-ramp) to all possible exit points (off-ramps) for both eastbound and westbound directions within the simulation model. Each route began at a routing decision point and ended at one or more destination points. For each designated route, a number of trips were as-signed based on the final O/D matrix explained earlier.

Figure 2: VISSIM Coded Network for I-10 and I-12.

Simulation experiments

Two simulation scenarios were created, one with ramp meters and one without ramp meters. For the ramp meter scenario, a ramp meter controller was added for each on-ramp along both corridors in both directions. Also, signal heads were installed at every on ramp to represent each ramp meter. A set of detectors was also attached to each signal head. One detector was placed at the location of the signal head and another one shortly be-hind signal head. Other detectors were added on each lane of the mainline to adjust the ramp meter flow rate based on the current lane occupancy detected on the mainline. Each set of detectors was identified with its reference signal head by a two-digit number system where the tens digit was the signal controller and the ones digit was the detector numbers. For the control corridor, no signal heads or detectors were created, as no ramp meters would be used. Vehicle Actuated Programming (VAP) was used as the signal state generator. With this setting, user controlled signal logic was actuated.

Simulation runs

In order to account for randomness in driving behavior, a total of 25 simulation runs were generated for each of the two scenarios. The network was simulated for one hour, in addition to a 15-minute warm up period. A set of network-level performance measures was also identified as follows:

  • Average delay time per vehicle [s]
  • Total travel time [h]
  • Statistical analysis

    This section presents the statistical analysis used to compare the traffic performance for metered and non-metered traffic on the study section. Basic descriptive statistics is presented first, followed by tests of hypothesis. All tests were performed in SAS®.

    Descriptive statistics

    The basic descriptive statistics for metered and non-metered scenarios is shown in Table 2. The statistics include the sample size, and the mean, standard deviation, and minimum and maximum values for the two performance measures. The table shows that when metering is implemented, the average delay per vehicle was reduced from 496 to 452 seconds. Same applies to the average travel time which was reduced from 6066 to 5292 veh. h. This indicates that the ramp metering improves the travel conditions in the corridor.

    Metered Corridors

    Variable

    N

    Mean

    Std Dev

    Min

    Max

    Average Delay Time per Vehicle [s]

    Travel Time [veh.h]

    24

    24

    452

    5292

    7.8

    33

    442

    5246

    463

    5346

    Non-Metered Corridors

    Average Delay Time per vehicle [s]

    Travel Time [veh.h]

    24

    24

    496

    6066

    5.3

    43

    484

    5959

    502

    6118

    Table 2: Basic Descriptive Statistics for Metered Corridors..

    Tests of hypothesis

    The statistical tool used for the analyses of results was the Student’s t-test for two independent population means with unknown variances. The population variances were first tested to confirm whether they were equal or not; so as to determine whether to perform a pooled t-test or Satterthwaite t-test, respectively. The test results for the average delay time per vehicle and total travel time are presented in the following sub-sections.

    Average delay time per vehicle: If μ i MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeqiVd0 2aaSbaaKqbGeaacaWGPbaabeaaaaa@3977@  and μ j MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeqiVd0 2aaSbaaKqbGeaacaWGQbaajuaGbeaaaaa@3A06@ denote the average delay time in seconds per vehicle for the metered and non-metered corridors respectively, the following hypotheses were tested:

  • H 0 : μ i μ j =0 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaamisam aaBaaajuaibaGaaGimaaqabaqcfaOaaiOoaiabeY7aTnaaBaaajuai baGaamyAaaqabaqcfaOaeyOeI0IaeqiVd02aaSbaaKqbGeaacaWGQb aabeaajuaGcqGH9aqpcaaIWaaaaa@4356@ (no difference exists)
  • H 1 : μ i μ j 0 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbiqaaGpejuaGca WGibWaaSbaaKqbGeaacaaIXaaabeaajuaGcaGG6aGaeqiVd02aaSba aKqbGeaacaWGPbaabeaajuaGcqGHsislcqaH8oqBdaWgaaqcfasaai aadQgaaeqaaKqbakabgcMi5kaaicdaaaa@4523@ (difference exists)
  • A test of variances concluded that both populations have equal variances. Therefore, a pooled t-test analysis was performed on the population means. This resulted in a p-value of <0.0001, which is less than the 0.05 level of significance used. It can therefore be concluded that at the 0.05 level of significance a difference exists between the average delay time between the metered and non-metered corridors. In particular, since μ i =452.26 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbiqaaGpejuaGcq aH8oqBdaWgaaqcfasaaiaadMgaaeqaaKqbakabg2da9iaaisdacaaI 1aGaaGOmaiaac6cacaaIYaGaaGOnaaaa@407D@  and μ j =496.93 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbiqaaGpejuaGcq aH8oqBdaWgaaqcfasaaiaadQgaaeqaaKqbakabg2da9iaaisdacaaI 5aGaaGOnaiaac6cacaaI5aGaaG4maaaa@408A@ , it can be concluded that the average delay time in seconds is greater for the non-metered corridor than that for the metered corridor. In other words, implementing ramp metering led to a statistically significant reduction in the average delay per vehicle.

    Total travel time: If μ i MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeqiVd0 2aaSbaaKqbGeaacaWGPbaabeaaaaa@3977@ and μ j MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaeqiVd0 2aaSbaaKqbGeaacaWGQbaajuaGbeaaaaa@3A06@  denoted the total travel time in vehicle hours for the metered and non-metered corridors, respectively, the following hypotheses were tested:

  • H 0 : μ i μ j =0 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcfaOaamisam aaBaaajuaibaGaaGimaaqabaqcfaOaaiOoaiabeY7aTnaaBaaajuai baGaamyAaaqabaqcfaOaeyOeI0IaeqiVd02aaSbaaKqbGeaacaWGQb aabeaajuaGcqGH9aqpcaaIWaaaaa@4356@   (no difference exists)
  • H 1 : μ i μ j 0 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbiqaaGpejuaGca WGibWaaSbaaKqbGeaacaaIXaaabeaajuaGcaGG6aGaeqiVd02aaSba aKqbGeaacaWGPbaabeaajuaGcqGHsislcqaH8oqBdaWgaaqcfasaai aadQgaaeqaaKqbakabgcMi5kaaicdaaaa@4523@ (difference exists)
  • Similar to the average delay analysis, the test of variances concluded that both populations shaves equal variances, and therefore, the pooled t-test analysis was performed on the total travel time too. The test resulted in a p-value of <0.0001, which is less than the 0.05 level of significance used. It can therefore be concluded that at the 0.05 level of significance a difference exists between the total travel time for the metered and non-metered corridors. In particular, since μ i =5,292 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbiqaaGpejuaGcq aH8oqBdaWgaaqcfasaaiaadMgaaeqaaKqbakabg2da9iaaiwdacaGG SaGaaGOmaiaaiMdacaaIYaaaaa@3FC0@  and μ j =6066 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbiqaaGpejuaGcq aH8oqBdaWgaaqcfasaaiaadQgaaeqaaKqbakabg2da9iaaiAdacaaI WaGaaGOnaiaaiAdaaaa@3F11@ , it can be concluded that the total travel time in vehicle hours is greater for the non-metered corridor than that for the metered corridor. In other words, the reduction in the total travel time resulted when implementing ramp metering is statistically significant.

    Conclusion


    The comparative evaluation of two scenarios (with and without ramp metering) showed a statistically significant improvement in the corridor performance when ramp metering strategies were implemented. The statistical analysis using the Student’s t-test for two independent samples with unknown variances showed consistently that the means were significantly different at 95% confidence level. A test of variances was also con-ducted and concluded that both populations had equal variances, and therefore, a pooled t-test analysis was conducted. More specifically, the statistical analysis shows that (a) the average delay time in seconds is greater for the non-metered corridor than that for the metered corridor; and (b) the total delay travel time in hours is greater for the non-metered corridor than that for the metered corridor. Based on the simulation results, the study endorses the use of ramp metering as a successful strategy for ICM”.

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