Queue Jump Lane, Transit Signal Priority, and Stop Location Evaluation of Transit Preferential Treatments Using Microsimulation

2015 ◽  
Vol 2533 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Burak Cesme ◽  
Selman Z. Altun ◽  
Barrett Lane
Keyword(s):  
Simulation Models ◽  
High Volume ◽  
Signal Timing ◽  
Transit Signal Priority ◽  
Preferential Treatment ◽  
Test Bed ◽  
Microscopic Simulation ◽  
Delay Reduction ◽  
Treatment Benefits ◽  
Detailed Simulation

Transit preferential treatments offer the potential to improve transit travel time and reliability. However, the benefits of these treatments vary greatly depending on the specific characteristics of the study area, including turning movement and pedestrian volumes, signal timing parameters, and transit stop location. To evaluate the performance of preferential treatments, practitioners typically rely on microscopic simulation models, which require a considerable amount of effort, or a review of previous studies, which may reflect a bias toward the area characteristics. This paper develops a test bed and a planning-level framework to help practitioners determine benefits offered by various preferential treatments without developing a detailed simulation model. To evaluate preferential treatment benefits, the authors performed extensive simulation runs under various scenarios at an isolated intersection with VISSIM. The analyses show that the greatest benefit comes from relocating a nearside stop to a farside stop, in which farside stops can reduce delay up to 30 s per intersection. The highest saving that could be obtained with a queue jump lane is approximately 9 s per intersection. As the number of right turns increases along with the number of conflicting pedestrians, the benefit of a queue jump lane disappears. Transit signal priority with 15 s of green extension and red truncation can offer up to 19 s of reduction in delay; the benefits become more pronounced with a high volume-to-capacity (v/c) ratio. With a low v/c ratio, granting 10 s of green extension without red truncation provides very marginal benefits; only a 2-s delay reduction per intersection is gained.

Using Automatic Calibration with Microscopic Traffic Simulation

2008 ◽  
Vol 12 (2) ◽  
pp. 106-110
Author(s):  
Iisakki Kosonen ◽  
Keyword(s):  
Error Function ◽  
Simulation Models ◽  
Automatic Calibration ◽  
Microscopic Simulation ◽  
Detailed Model ◽  
Microscopic Traffic Simulation ◽  
Calibration And Validation ◽  
University Of Technology ◽  
Detailed Simulation

The microscopic simulation is getting increasingly common in traffic planning and research because of the detailed analysis it can provide. The drawback of this development is that the calibration and validation of such a detailed simulation model can be very tedious. This paper summarizes the research on automatic calibration of a high-fidelity micro-simulation (HUTSIM) at the Helsinki University of Technology (TKK). In this research we used ramp operation as the case study. The automatic calibration of a detailed model requires a systematic approach. A key issue is the error-function, which provides a numeric value to the distance between simulated and measured results. Here we define the distance as combination of three distributions namely the speed distribution, gap distribution and lane distribution. We developed an automated environment that handles all the necessary operations. The system organizes the files, executes the simulations, evaluates the error and generates new parameter combinations. For searching of the parameter space we used a genetic algorithm (GA). The overall results of the research were good demonstrating the potential of using automatic processes in both calibration and validation of simulation models.

Microscopic Simulation Model Calibration and Validation: Case Study of VISSIM Simulation Model for a Coordinated Actuated Signal System

2003 ◽  
Vol 1856 (1) ◽  
pp. 185-192 ◽  
Author(s):  
Byungkyu (Brian) Park ◽  
J. D. Schneeberger
Keyword(s):  
Data Collection ◽  
Simulation Model ◽  
Model Calibration ◽  
Simulation Models ◽  
Test Bed ◽  
Microscopic Simulation ◽  
Calibration And Validation ◽  
Parameter Values ◽  
Model Calibration And Validation

Microscopic simulation models have been widely used in both transportation operations and management analyses because simulation is safer, less expensive, and faster than field implementation and testing. While these simulation models can be advantageous to engineers, the models must be calibrated and validated before they can be used to provide meaningful results. However, the transportation profession has not established any formal or consistent guidelines for the development and application of these models. In practice, simulation model–based analyses have often been conducted under default parameter values or bestguessed values. This is mainly due to either difficulties in field data collection or lack of a readily available procedure for simulation model calibration and validation. A procedure was proposed for microscopic simulation model calibration and validation and an example case study is presented with real-world traffic data from Route 50 on Lee Jackson Highway in Fairfax, Virginia. The proposed procedure consisted of nine steps: ( a) measure of effectiveness selection, ( b) data collection, ( c) calibration parameter identification, ( d) experimental design, ( e) run simulation, ( f) surface function development, ( g) candidate parameter set generations, ( h) evaluation, and ( i) validation through new data collection. The case study indicates that the proposed procedure appears to be properly calibrating and validating the VISSIM simulation model for the test-bed network.

Modeling Passenger Behavior in Nonpayment Areas at Rail Transit Stations

2015 ◽  
Vol 2534 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Mingjun Liao ◽  
Gang Liu
Keyword(s):  
Choice Model ◽  
Transition Probability ◽  
Simulation Models ◽  
Critical Issue ◽  
Microscopic Simulation ◽  
Movement Model ◽  
New Ideas ◽  
Path Navigation ◽  
Pass Through ◽  
Passenger Behavior

The nonpayment area at urban transit stations in China usually becomes extremely crowded during peak hours because many passengers queue to buy tickets and pass through the fare gates. How to evaluate the performance of these activities is a critical issue for the design and management of the nonpayment area. This study used microscopic simulation models to investigate passenger behavior in the nonpayment area. The study developed a queue choice model, a passenger movement model, and a path navigation model. Some new ideas were involved. First, the study introduced the concepts of dynamic queue length and dynamic distance between the current passenger and alternative queues into the queue choice model. Second, a new factor, called direction of goal, was proposed to navigate a passenger through the dynamic end of a queue or other goals. This factor was used to construct the transition probability function of a cellular automata model. Finally, the proposed models were calibrated and verified on the basis of a field survey and sensitivity analysis. The results show that the proposed models can capture passenger behaviors in the nonpayment area and perform well for queue estimation.

Modeling and Estimation of Headway Distributions

1997 ◽  
Vol 1591 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Serge P. Hoogendoorn ◽  
Hein Botma
Keyword(s):  
Estimation Method ◽  
Simulation Models ◽  
Independent Random Variables ◽  
Microscopic Simulation ◽  
Simple Analysis ◽  
Rural Roads ◽  
The Road ◽  
Road Capacity ◽  
Measurement Location ◽  
Fourier Series Analysis

A simple analysis to derive Branston’s generalized queueing model for (time-) headway distributions is presented. It is assumed that the total headway is the sum of two independent random variables: the empty zone and the free-flowing headway. The parameters of the model can be used to examine various characteristics of both the road (e.g., capacity) and driver-vehicle combinations (e.g., following behavior). Furthermore, the model can be applied to vehicle generation in microscopic simulation models and to safety analysis. To estimate the different parameters in the model, a new estimation method is proposed. This method, which was developed on the basis of Fourier-series analysis, was successfully applied to measurements collected on two-lane rural roads. The method was found to be both computationally less demanding and more robust than traditional parameter techniques procedures, such as maximum likelihood. In addition, the method provides more accurate results. Parameters in the model were examined with the developed estimation method. Estimates of these parameters at a specific period and a specific measurement location were to some extent transferable to other periods and locations. Application of the method to road capacity estimation is discussed.

Regression Analysis of Predictions and Forecasts of Cloud Data Centre KPIs Using the Boosted Decision Tree Algorithm

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera
Keyword(s):  
Decision Tree ◽  
Heterogeneous Computing ◽  
Learning Algorithm ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Cloud Data ◽  
On Demand ◽  
Boosted Decision Tree ◽  
Data Centres

<div>The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.</div>

Hider: A Methodology for Early-Stage Exploration of Design Space

1996 ◽  
Author(s):  
Sudhakar Y. Reddy
Keyword(s):  
Design Space ◽  
Optimization Problems ◽  
Early Stage ◽  
Simulation Models ◽  
Design Tool ◽  
System Level ◽  
Complex Simulation ◽  
Machine Learning Approach ◽  
Detailed Simulation ◽  
And Performance

Abstract This paper describes HIDER, a methodology that enables detailed simulation models to be used during the early stages of system design. HIDER uses a machine learning approach to form abstract models from the detailed models. The abstract models are used for multiple-objective optimization to obtain sets of non-dominated designs. The tradeoffs between design and performance attributes in the non-dominated sets are used to interactively refine the design space. A prototype design tool has been developed to assist the designer in easily forming abstract models, flexibly defining optimization problems, and interactively exploring and refining the design space. To demonstrate the practical applicability of this approach, the paper presents results from the application of HIDER to the system-level design of a wheel loader. In this demonstration, complex simulation models for cycle time evaluation and stability analysis are used together for early-stage exploration of design space.

Parameter Sensitivity Analysis of a Cooperative Dynamic Bus Lane System With Connected Vehicles

2021 ◽  
pp. 036119812110357
Author(s):  
Meng Xie ◽  
Michael Winsor ◽  
Tao Ma ◽  
Andreas Rau ◽  
Fritz Busch ◽  
...  
Keyword(s):  
Travel Time ◽  
Transition Period ◽  
Penetration Rate ◽  
Simulation Models ◽  
Driving Behavior ◽  
Connected Vehicles ◽  
Microscopic Simulation ◽  
Communication Range ◽  
Bus Lane ◽  
The Impact

This paper aims to evaluate the sensitivity of the proposed cooperative dynamic bus lane system with microscopic traffic simulation models. The system creates a flexible bus priority lane that is only activated on demand at an appropriate time with advanced information and communication technologies, which can maximize the use of road space. A decentralized multi-lane cooperative algorithm is developed and implemented in a microscopic simulation environment to coordinate lane changing, gap acceptance, and car-following driving behavior for the connected vehicles (CVs) on the bus lane and the adjacent lanes. The key parameters for the sensitivity study include the penetration rate and communication range of CVs, considering the transition period and gradual uptake of CVs. Multiple scenarios are developed and compared to analyze the impact of key parameters on the system’s performance, such as total saved travel time of all passengers and travel time variation among buses and private vehicles. The microscopic simulation models showed that the cooperative dynamic bus lane system is significantly sensitive to the variations of the penetration rate and the communication range in a congested traffic state. With a CV system and a communication range of 150 m, buses obtain maximum benefits with minimal impacts on private vehicles in the study simulation. The safety concerns induced by cooperative driving behavior are also discussed in this paper.

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