Validating HCS and SIDRA Software for Estimating Delay at Signalized Intersections in Jordan

The main objective of this research was to validate the Highway Capacity Software (HCS) and the Signalized and Unsignalized Intersection Design and Research Aid (SIDRA) Software which are widely used for estimating delay at signalized intersections in Jordan. Fifty four hours of data from eighteen legs at five signalized intersections were collected from Irbid city; the second largest city in Jordan. Traffic volumes and control delay were measured during peak and off-peak periods using video cameras. Data on geometric design elements, signal timings and phasings and vehicular speeds were measured through a field survey. The results of the analysis indicated that both models can be improved significantly by calibrating the basic saturation flow rate or bus PCE factor. However, the best improvement was obtained by calibrating both the basic saturation flow rate and the bus PCE factor simultaneously. It was also found that the two software are good predictors for control delay at signalized intersections in Jordan after calibration. However, SIDRA was found to be better than HCS 2000

Calibrating and modelling of statistical delay for signalized intersections at al-Nasiriyah City in Iraq

In this paper develops an empirical delay model for delay prediction by taking lane group parameters to make delay model in the field where delay have considered very important measure that effects at signalized intersection because of relation of delay with performance of signalized intersection, lost travel time, fuel consumption, feasible of movement, discomfortable of drivers also, it is considered the primary measure to determine the level of service at signalized intersection. The main aim of this study is to make a field delay model at signalized intersection by using microsimulation software to calibrate data and using statistical software (SPSS) to create model. The methodology of the study is made by using video recording and manual collected data by taken three biggest signalized intersections where the collection data was very challenged specifically speed forward and uncontrollable drivers and others factors. Sidra Intersection 6.0 is described as an advanced micro-analytical model with a lane-by-lane method and a vehicle drive-cycle model that is used to estimate capacity and performance measures through an iterative method. Calibration of the software is very necessary to find accurate model that can be described the field delay. Multiple Linear Regression analysis (MuLRa) has been generally statistical method to create a model. It has been taken into consideration in the modelling of delay at signalized intersection and adjusted R² is 80% with multi factors that effect on field delay. Vehicle speed has been improved very significance level and impact factor on delay at signalized intersection by lane group experimental method and this finding very important for all simulation software should be taken that in the accounts

Analysis Of Queue Characteristics At Signalized Intersections Near Highway-Railroad Grade Crossing

Analysis of traffic queues at signalized intersections which are in close proximity to highway- railroad grade crossings is of primary importance for determining if the normal signal operation needs to be preempted for railroad operations by providing a special signal mode for safe clearance of the queued vehicles from the tracks before the train arrival, and prohibiting any conflicting traffic movements towards the crossing. Such queuing analysis becomes even more critical where direct observations of traffic queues are not possible or where the assessment is needed for a future location. Inadequate estimation of queues from signalized intersections to the nearby railroad grade crossing can lead to severe safety issues. Underestimation of queue lengths may lead to an unsafe design while significantly overestimated queues may cause unnecessary traffic delays consequently leading to violations of the active traffic control devices at the crossing. In order to determine an adequate approach for reasonable estimation of queue lengths at signalized intersections near highway-railroad grade crossings, this dissertation first evaluated and compared different currently used microscopic simulation-based methods (i.e. Sim-Traffic and VISSIM) for their adequacy in estimating the queue lengths. After that several comparisons are made between the queue estimation from the simulation-based and other deterministic analytical methods including Highway Capacity Software, Synchro, and Railroad Assessment Tool. The comparisons drawn between each method helped identifying the differences and specific limitations of each method in including the impact of various important factors on the resulting queue estimation. The recommendations are provided on the basis of model capability to adequately count the impact of various significant traffic factors on queue estimation and considering minimizing the risk of underestimated queues. Based on the analysis findings, a microscopic simulation based procedure is developed using Sim-Traffic for estimating the 95th percentile queue lengths on various existing signalized intersection configurations near highway-rail grade crossings to help evaluate the need for signal preemption. In addition, recommendations are developed, if preemption is necessary, for determining queue clearance distance and minimum track clearance time. The recommended procedure is developed considering minimizing the risk of underestimated queues or unsafe design at such locations, and simplify the design and decision-making process

Evaluating the Impact of Double-Parked Freight Deliveries on Signalized Arterial Control Delay Using Analytical Models and Simulation

Freight deliveries on signalized urban streets are known to cause lane blockages during delivery. Traffic congestion associated with urban freight deliveries has gained increasing attention recently as traffic engineers and planners are tasked with finding solutions to manage increasing demand more sustainably with limited road capacity. The goal of this research is to evaluate two models for quantifying the capacity and signalized control delay effects of a lane-blocking freight delivery on an urban arterial. The two methods are: an All-or-Nothing model similar to methodology used in the Highway Capacity Manual 6th Edition, and a Detailed model consistent with kinematic wave theory. The purpose is to provide insight on the use of these tools for analysis of urban freight delivery. The signalized control delay results of the two models are compared with observed video data of urban deliveries from one city block of 8th Ave in New York City. Empirical confirmation of double-parked delivery impact on signalized controlled delay remains elusive due to an inability to isolate the effects of the deliveries from other traffic perturbations in the video sample. Instead, microscopic simulation using Aimsun is used for comparison to the theoretical models and the results lend credibility to the Detailed model. The simulation results show a similar trend of delay impact from double-parked deliveries located at a range distances from the intersection and more closely resembled the Detailed model. The All-or-Nothing model would provide only a coarse representation of the capacity and delay effects. The more detailed approach that accounts for the dynamics of queuing in front of the delivery vehicle provides closed form analytical formulas for capacity and signalized control delay that can account for varying locations of deliveries as well as analysis periods with some blocked cycles and others unblocked. Two policy implications are proposed: 1) that double-parked deliveries located mid-block likely result in less signalized control delay impact, and 2) freight receivers that attract double-parked deliveries near an intersection stop line should be prioritized in urban freight delivery mitigation policies such as off-hour delivery

Multi-resolution Modeling of Dynamic Signal Control on Urban Streets

Dynamic signal control provides significant benefits in terms of travel time, travel time reliability, and other performance measures of transportation systems. The goal of this research is to develop and evaluate a methodology to support the planning for operations of dynamic signal control utilizing a multi-resolution analysis approach. The multi-resolution analysis modeling combines analysis, modeling, and simulation (AMS) tools to support the assessment of the impacts of dynamic traffic signal control. Dynamic signal control strategies are effective in relieving congestions during non-typical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. Although, an important aspect when modeling incident responsive signal control is to determine the capacity impacts of incidents considering the interaction between the drop in capacity below demands at the midblock urban street segment location and the upstream and downstream signalized intersection operations. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. A second model is developed to estimate the reduction in the upstream intersection capacity due to the drop in capacity at the midblock incident location as estimated by the first model. These developed models are used as part of a mesoscopic simulation-based DTA modeling to set the capacity during incident conditions, when such modeling is used to estimate the diversion during incidents. To supplement the DTA-based analysis, regression models are developed to estimate the diversion rate due to urban street incidents based on real-world data. These regression models are combined with the DTA model to estimate the volume at the incident location and alternative routes. The volumes with different demands and incident levels, resulting from DTA modeling are imported to a microscopic simulation model for more detailed analysis of dynamic signal control. The microscopic model shows that the implementation of special signal plans during incidents and different demand levels can improve mobility measures

Multi-resolution Modeling of Dynamic Signal Control on Urban Streets

Dynamic signal control provides significant benefits in terms of travel time, travel time reliability, and other performance measures of transportation systems. The goal of this research is to develop and evaluate a methodology to support the planning for operations of dynamic signal control utilizing a multi-resolution analysis approach. The multi-resolution analysis modeling combines analysis, modeling, and simulation (AMS) tools to support the assessment of the impacts of dynamic traffic signal control. Dynamic signal control strategies are effective in relieving congestions during non-typical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. Although, an important aspect when modeling incident responsive signal control is to determine the capacity impacts of incidents considering the interaction between the drop in capacity below demands at the midblock urban street segment location and the upstream and downstream signalized intersection operations. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. A second model is developed to estimate the reduction in the upstream intersection capacity due to the drop in capacity at the midblock incident location as estimated by the first model. These developed models are used as part of a mesoscopic simulation-based DTA modeling to set the capacity during incident conditions, when such modeling is used to estimate the diversion during incidents. To supplement the DTA-based analysis, regression models are developed to estimate the diversion rate due to urban street incidents based on real-world data. These regression models are combined with the DTA model to estimate the volume at the incident location and alternative routes. The volumes with different demands and incident levels, resulting from DTA modeling are imported to a microscopic simulation model for more detailed analysis of dynamic signal control. The microscopic model shows that the implementation of special signal plans during incidents and different demand levels can improve mobility measures

Evaluation of Impacts on Delay, Cycle-Length Optimization, Control Types, and Peak-Hour Factor with the Randomness of Traffic

Some basic concepts about traffic which are correct in theory may be misinterpreted in practice. Such misinterpretations may lead to a different direction from the ideal operation. This four-part dissertation is designated to examine fundamental concepts in traffic operation, and to validate the impact of randomness on control delays, cycle-length optimization, control types, and the peak-hour factor. Control delays experienced by drivers is a critical performance measure on interrupted-flow traffic which involves movements at slower speeds and stops on intersection approaches, as vehicles move up in the queue or slow down upstream of an intersection. Since the basic term of control delay in a signalized intersection was originally from queueing analyses within a cycle, results from such models may be inaccurate due to the neglect of inter-cycle traffic variation. Besides, traffic is rare varying on the clock. Therefore, the peak-hour factor will be inaccurate to a certain degree if peak periods are placed on the clock. All parts of this dissertation, except the first and the last, are independent papers for different professional journals, and are summarized as follows. Part II of this dissertation, “Impacts of Inter-Cycle Demand Fluctuations on Delay”, distinguishes between intra- and inter-cycle demand fluctuations and recognizes the potentially significant impact of delay underestimation when inter-cycle demand fluctuation is unaccounted for, as in all previous models. “Short or Long … which is Better? A Probabilistic Approach towards Cycle Length Optimization” in the third part of this dissertation proposes a framework to determine the optimal or near-optimal cycle length for signalized intersections based on the criterion with minimal control delays. The fourth part with title “A Trade-Off Framework for Determining the Best Control at an Intersection” in this dissertation uses the same criterion with minimal control delays to assist decision makers in the trade-off between signals and stop signs for an intersection. Part V of this dissertation, “Impacts of Misplaced Peak Intervals on PHFs”, argues about the significant difference among different ways to define the peak intervals, and distinguishes the differences between the “real” and “on the clock” peak-hour factors

Estimating vehicles emissions at signalized intersections in the highway capacity manual

Over the past decades, motor vehicle volumes have continued to increase at a high rate. As a result, engineers in the transportation field not only need more robust knowledge of traffic operation control and transportation planning, but more attention is also needed to understand and estimate the influences that this increasing volume of vehicles has on the environment, especially the influence on air quality. The EPA has stated that reducing carbon monoxide (CO) from vehicle emissions is the most significant way to control air pollution from the transportation sector. The Highway Capacity Manual is a national and international resource that has become a guideline for evaluating the operation of roadway, transit and pedestrian facilities. The Highway Capacity Manual assesses the operation of a roadway based on the perception of its users. Performance measures are used to describe the traffic operation of the roadway. At present, no measures are provided to describe the operation of the roadway based on environmental impacts. The incorporation of air pollution estimation into the Highway Capacity Manual will allow the roadway’s operation to be assessed both from an operational and environmental aspect, ultimately creating a sustainable development for both transportation and the environment. The objective of this dissertation is to develop MOVES-like estimation models of vehicle emissions for pollutants at a signalized intersection that can be incorporated into the Highway Capacity Manual. “EPA’s Motor Vehicle Emission Simulator (MOVES) is a state-of-the-art emission modeling system that estimates emissions for mobile sources at the national, county, and project level for criteria air pollutants, greenhouse gases, and air toxics.” (EPA, 2014). A thorough understanding is needed about what parameters, and influence of these parameters on vehicle emissions. This dissertation develops two kinds of models in order to estimate emissions caused by on-road vehicles. Two modeling approaches are used to estimate four kinds of emissions including CO, NO, NH3 and NOX separately. The following summarizes the work of this dissertation: The objective of this dissertation is to develop MOVES-like estimation models of vehicle emissions for pollutants at a signalized intersection that can be incorporated into the Highway Capacity Manual. “EPA’s Motor Vehicle Emission Simulator (MOVES) is a state-of-the-art emission modeling system that estimates emissions for mobile sources at the national, county, and project level for criteria air pollutants, greenhouse gases, and air toxics.” (EPA, 2014). A thorough understanding is needed about what parameters, and influence of these parameters on vehicle emissions. This dissertation develops two kinds of models in order to estimate emissions caused by on-road vehicles. Two modeling approaches are used to estimate four kinds of emissions including CO, NO, NH3 and NOX separately. The following summarizes the work of this dissertation: (1) Two modeling approaches are used to estimate vehicle emissions including: multiple linear regression and Artificial Neural Network (ANN). In the multiple linear regression modeling, two different models were developed including one model using operation modes as independent variables and another model using traffic related parameters as independent variables. Both model approaches and independent variables are used to estimate four types of pollutant emissions. Statistically, the emission models using traffic parameters as independent HCM related parameters are capable of providing a better emissions estimate based on the higher R square value. For CO, the variables found to be significant were volume to capacity ratio and grade with an R2 of 61.56%. For NO, the variables found to be significant were volume to capacity ratio and grade with an R2 of 99.47%. For NOx, the variables found to be significant were volume to capacity ratio and grade with an R2 of 99.47%. For NH3, the variables found to be significant were volume to capacity ratio and grade with an R2 of 99.25%. This study shows that volume to capacity dominate the emissions quality at a signalized intersection. The research found that for NOx, Idling and Moderate Speed Coasting were significant. For NH3, all variables were significant except Low Speed Coasting. For CO, Braking and Cruise/Acceleration were significant. It was also found that longer delay time reduces CO emissions, but it causes the other three pollutant emissions increase. (2) The ANN modeling method using the Levenberg-Marquardt method was used to train the HCM related variables and MOVES emissions outputs. The parameters of volume to capacity ratio, and road grade are used to estimate emissions. The Validated R value of the obtained ANN model is found

What Is an Effective Way to Measure Arterial Demand When It Exceeds Capacity?

This project focused on developing and evaluating methods for estimating demand volume for oversaturated corridors. Measuring demand directly with vehicle sensors is not possible when demand is larger than capacity for an extended period, as the queue grows beyond the sensor, and the flow measurements at a given point cannot exceed the capacity of the section. The main objective of the study was to identify and develop methods that could be implemented in practice based on readily available data. To this end, two methods were proposed: an innovative method based on shockwave theory; and the volume delay function adapted from the Highway Capacity Manual. Both methods primarily rely on probe vehicle speeds (e.g., from INRIX) as the input data and the capacity of the segment or bottleneck being analyzed. The proposed methods were tested with simulation data and validated based on volume data from the field. The results show both methods are effective for estimating the demand volume and produce less than 4% error when tested with field data

Updated Methods for Traffic Impact Analysis, Including Evaluation of Innovative Intersection Designs: Volume II—Applicant’s Guide

The INDOT Applicant’s Guide to Traffic Impact Analysis (TIA) is a product of SPR-3605 Updated Methods for Traffic Impact Analysis . The purpose of this study was to review the Applicant’s and Reviewer’s Guides that were published in 1992 and make changes that would bring them in line with the methods and conditions that have emerged since then. This guide is intended to establish a standard framework for traffic impact analysis within Indiana, increasing consistency in study requests, preparation and review. A standardized procedure will enable the TIA study preparer to present the study findings and recommendations in a systematic manner consistent with the reviewer\u27s expectations. The guide is not intended to make things more complicated and time-consuming. On the contrary, with a standard framework, the time involved in the process will decrease for both parties. The Applicant\u27s Guide allows enough flexibility to the study preparer to use innovative methods based on sound engineering judgment and the conditions at a specific site. However, this should be done with the prior consent of the study reviewer(s)