Hesham Rakha

DEVELOPMENT OF VT-MICRO MODEL FOR ESTIMATING HOT STABILIZED LIGHT DUTY VEHICLE AND TRUCK EMISSIONS

Abstract The paper applies a framework for developing microscopic emission models (VT-Micro model version 2.0) for assessing the environmental impacts of transportation projects. The original VT-Micro model was developed using chassis dynamometer data on nine light duty vehicles. The VT-Micro model is expanded by including data from 60 light duty vehicles and trucks. Statistical clustering techniques are applied to group vehicles into homogenous categories. Specifically, classification and regression tree algorithms are utilized to classify the 60 vehicles into 5 LDV and 2 LDT categories. In addition, the framework accounts for temporal lags between vehicle operational variables and measured vehicle emissions. The VT-Micro model is validated by comparing against laboratory measurements with prediction errors within 17%.

Comparison of delay estimates at under-saturated and over-saturated pre-timed signalized intersections

Delay is an important parameter that is used in the optimization of traffic signal timings and the estimation of the level of service at signalized intersection approaches. However, delay is also a parameter that is difficult to estimate. While many methods are currently available to estimate the delays incurred at intersection approaches, very little research has been conducted to assess the consistency of these estimates. This paper addresses this issue by comparing the delays that are estimated by a number of existing delay models for a signalized intersection approach controlled in fixed-time and operated in a range of conditions extending from under-saturated to highly saturated. Specifically, the paper compares the delay estimates from a deterministic queuing model, a model based on shock wave theory, the steady-state Webster model, the queue-based models defined in the 1981 Australian Capacity Guide, the 1995 Canadian Capacity Guide for Signalized Intersections, and the 1994 and 1997 versions of the Highway Capacity Manual (HCM), in addition to the delays estimated from the INTEGRATION microscopic traffic simulation software. The results of the comparisons indicate that all delay models produce similar results for signalized intersections with low traffic demand, but that increasing differences occur as the traffic demand approaches saturation. In particular, it is found that the delay estimates from the INTEGRATION microscopic simulation model generally follow the delay estimates from the time-dependent models defined in the 1997 HCM, 1995 Canadian Capacity Guide, and 1981 Australian Capacity Guide over the entire range of traffic conditions considered.

Requirements for Evaluating Traffic Signal Control Impacts on Energy and Emissions Based on Instantaneous Speed and Acceleration Measurements

The evaluation of many transportation network improvements commonly is conducted by first estimating average speeds from a transportation or traffic model and then converting these average speeds into emission estimates based on an environmental model such as MOBILE. Unfortunately, recent research has shown that certainly average speed and perhaps even simple estimates of the amount of delay and the number of stops on a link are insufficient measures to fully capture the impact of intelligent transportation system strategies such as traffic signal coordination. In an attempt to address this limitation, the application of a series of multivariate fuel consumption and emission prediction models is illustrated, both within a traffic simulation model of a signalized arterial and directly to instantaneous speed and acceleration data from floating cars traveling down a similar signalized arterial. The application of these multivariate relationships is illustrated for eight light-duty vehicles, ranging in size from subcompacts to minivans and sport-utilities using data obtained from the Oak Ridge National Laboratory. The objective is to illustrate that the application of these instantaneous models is both feasible and practical and that it produces results that are reasonable in terms of both their absolute magnitude and their relative trends. This research is one step in a more comprehensive modeling framework for dealing with the impacts of intelligent transportation systems on energy consumption and vehicle emissions. Other steps include analyses of traffic diversion and induced demand and validation of the estimated fuel consumption and emissions using direct on-road measurements.

Characterizing Driver Behavior on Signalized Intersection Approaches at the Onset of a Yellow-Phase Trigger

This paper involves a field test on 60 test participants to characterize driver behavior (perception-reaction time (PRT) and stopping/running decisions) at the onset of a yellow phase. Driver behavior is analyzed for five trigger distances that are measured from the vehicle position at the start of the yellow indication to the stop bar. This paper demonstrates that the 1.0-s 85th-percentile PRT that is recommended in traffic-signal-design procedures is valid and consistent with the field observations. Furthermore, this paper clearly shows that brake PRTs are impacted by the vehicles time to intersection (TTI) at the onset of a yellow-indication introduction. This paper also demonstrates that either a lognormal or a beta distribution is sufficient to model the stochastic nature of the brake PRT. In terms of stopping decisions, this paper demonstrates that the probability of stopping varies from 100% at a TTI of 5.5 s to 9% at a TTI of 1.6 s. This paper also indicates a decrease in the probability of stopping for male drivers when compared with female drivers. Furthermore, this study suggests that drivers 65 years of age and older are significantly less likely to clear the intersection at short yellow-indication trigger distances when compared with other age groups. The dilemma zone for the less than 40 year old group is found to range from 3.9 to 1.85 s, whereas the dilemma zone for the greater than 70 year old group is found to range from 3.2 to 1.5 s.

Eco-driving at signalized intersections using V2I communication

The research presented in this paper develops a framework to enhance vehicle fuel consumption efficiency while approaching a signalized intersection through the provision of signal phase and timing information that may be available through vehicle-to-infrastructure communication. While past research uses simplified objective functions to optimize fuel consumption and/or emissions caused by signalized intersections, this research highlights the importance of retaining microscopic fuel consumption models in the optimization function. It presents an example which shows that simplified objective functions may result in erroneous conclusions.

Comparison of Greenshields, Pipes, and Van Aerde Car-Following and Traffic Stream Models

Three car-following models were compared: the Greenshields single-regime model, the Pipes two-regime model, and a four-parameter single-regime model that amalgamates both the Greenshields and Pipes models. The four-parameter model proposed by Van Aerde and Rakha is less known but is currently implemented in the INTEGRATION 2.30 software. The Greenshields and Pipes models were considered because they represent state-of-the-practice models for several types of microscopic and macroscopic software. The Greenshields model is widely used in macroscopic transportation planning models. In addition, the Pipes model is implemented in a number of microscopic traffic simulation models including CORSIM and VISSIM. Steady-state car-following behavior is also related to macroscopic traffic stream models to develop calibration procedures that can be achieved using macroscopic loop detector data. The study concluded that the additional degree of freedom that results from including a fourth parameter (Van Aerde model) overcomes the shortcomings of the current state-of-the-practice traffic stream models by capturing both macroscopic and microscopic steady-state traffic behavior for a wide range of roadway facilities and traffic conditions. Also developed was a procedure for calibrating the Pipes car-following model using macroscopic field measurements that can be obtained from loop detectors. Although this calibration procedure does not overcome the inherent shortcomings of the Pipes model, it does provide an opportunity to calibrate the CORSIM and VISSIM car-following behavior to existing roadway conditions more efficiently and without the need to collect microscopic traffic data.

Vehicle dynamics model for estimating maximum light-duty vehicle acceleration levels

A vehicle dynamics model for predicting maximum light-duty vehicle accelerations for use within a microscopic traffic simulation environment is presented and validated. The research also constructs a database of unconstrained vehicle acceleration data for 13 light-duty vehicles and trucks. With the use of the field data, the proposed vehicle dynamics model is validated and compared with a number of state-of-the-art vehicle acceleration models, including the Searle model and the dual-regime, linear decay, and polynomial models. The advantages of the proposed model include its ability to predict vehicle behavior accurately with readily available input parameters and its flexibility in estimating acceleration rates of both large and small vehicles on varied types of terrain.

Estimating Path Travel-Time Reliability

The estimation of path or trip travel-time reliability is critical to any advanced traveler information system. The state-of-practice procedures for estimating path travel-time reliability assumes that travel times follow a normal distribution and requires a measure of trip travel-time variance. The study analyzes AVI data from San Antonio and demonstrates through goodness-of-fit tests that the assumption of normality is, from a theoretical standpoint, inconsistent with field travel-time observations and that a lognormal distribution is more representative of roadway travel times. However, visual inspection of the data demonstrates that the normality assumption may be sufficient from a practical standpoint given its computational simplicity. The paper then proposes five methods for the estimation of path travel-time variance from its component segment travel-time variances. The analysis demonstrates that computing the trip travel-time coefficient of variation as the conditional expectation over all realizations of roadway segments provides estimates within 13% of field observations for both uncongested and congested conditions

Construction and Calibration of a Large-Scale Microsimulation Model of the Salt Lake Area

The objective of this paper is threefold. First, the feasibility of modeling a large-scale network at a microscopic level of detail is presented. Second, the unique data collection challenges that are involved in constructing and calibrating a large-scale network microscopically are described. Third, the unique opportunities and applications from the use of a microscopic as opposed to a macroscopic simulation tool are described. The possibility and feasibility of modeling a large-scale network using a microscopic simulation model is demonstrated. The requirements of a validated microscopic model for large-scale modeling are: (a) the model must be capable of modeling origin-destination demand tables, (b) the model must be capable of modeling dynamic traffic routing, and (c) the model must be capable of modeling the dynamic interaction of freeway/arterial facilities. The data collection and coding exercise for microscopic models is more intensive than for macroscopic models. The calibration exercise for a microscopic model to a large-scale network, although feasible, is by no means an easy task and does require expert assistance. The Salt Lake metropolitan region study has demonstrated that the data collection, coding, and calibration exercise is approximately a 4-person-year exercise. Model execution times during peak periods are still quite high (from 2 to 17 times the simulation time depending on the number of vehicles) for the PC platform (Pentium 200 with 64 megabytes of random-access memory). Consequently, tools that can extract portions of the large-scale network can allow the modeler to conduct various types of sensitivity analyses within a more realistic time frame.

Energy and Environmental Impacts of Roadway Grades

Although roadway grades are known to affect vehicle fuel consumption and emissions rates, there do not appear to be any systematic evaluations of these impacts in the literature. Consequently, this paper addresses this void by offering a systematic analysis of the impact of roadway grades on vehicle fuel consumption and emissions rates by using the INTEGRATION microscopic traffic simulation software. The energy and emissions impacts are quantified for various cruising speeds, under stop-and-go conditions, and for various traffic signal control scenarios. The study demonstrates that the impact of roadway grade is significant, with increases in vehicle fuel consumption and emissions rates in excess of 9% for a 1% increase in roadway grade. Consequently, a reduction in roadway grades in the range of 1% can offer savings that are equivalent to those offered by various advanced traffic management systems.