Adel W. Sadek

An Evaluation of Environmental Benefits of Time-Dependent Green Routing in the Greater Buffalo-Niagara Region

This article conducts a realistic assessment, using a real-world case study, of the likely environmental benefits of an intelligent transportation systems (ITS) strategy that provides dynamic route guidance to travelers based on the lowest emission or fuel consumption routes. Several features distinguish this study from previous reported research. First, the research utilizes a realistic case study of a medium-sized metropolitan area in the United States. Second, the research applies the latest state-of-the-art models on both the transportation and the environmental modeling sides, through the development of an integrated model combining the Transportation Analysis and Simulation System (TRANSIMS) model and the Multi-Scale Motor Vehicle Emissions Simulator model (MOVES). The integrated model is then used to approximate “green user equilibrium,” and to investigate the impact of market penetration on the likely environmental benefits of green routing. Third, the article also proposes a “targeted” traveler selection strategy whereby travelers with the greatest potential to reduce emissions or fuel consumption are selected for rerouting. Results indicate that green routing could result in significant reductions in emissions, but that this naturally comes at the expense of an increased travel time. For one scenario that considered assuming a traffic stream of only passenger cars, green routing resulted in an almost 13% reduction in carbon monoxide (CO) emissions, and a corresponding 8% increase in travel time. The results also indicate that tangible emissions reductions are achievable at low to medium market penetration levels for green routing applications, especially when travelers with the largest likely emissions savings are targeted.

Integrated Traffic-Driving-Networking Simulator for the Design of Connected Vehicle Applications: Eco-Signal Case Study

This article first develops an integrated traffic-driving-networking simulator (ITDNS) intended for the design and evaluation of cyber transportation systems (CTS) and connected vehicle (CV) applications. The ITDNS allows a human driver to control a subject vehicle, in a virtual environment, that is capable of communicating with other vehicles and the infrastructure with CTS messages. The challenges associated with the integration of the three simulators, and how those challenges were overcome, are discussed. As an application example, an eco-signal system, which recommends the approach speed for vehicles approaching the intersection so as to minimize fuel consumption and emissions, was implemented in the ITDNS. Test drivers were then asked to virtually drive through a signalized corridor twice, one time with the eco-signal system in place and another without the system. Thanks to the human-in-the-loop component of ITDNS, the research was able to evaluate the likely benefits of the eco-signal system, while accounting for the response of human drivers to the recommended speed profiles. Moreover, the study compared the energy consumption and emission production rates of human-controlled vehicles’ approach trajectories to the rates associated with “idealistic” trajectories that may be attainable via vehicle automation. With respect to ITDNS, the study demonstrates the unique advantages of the simulator and the broad range of applications it can address. Regarding the eco-signal application example, preliminary results demonstrate the potential of the concept to result in tangible reductions of around 9% for energy consumption, 18% for carbon monoxide, and 25% for nitrogen oxides emissions. Moreover, the application eliminated hard accelerations and decelerations maneuvers, and thus may have an additional positive safety impact.

A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a time-to-an-event is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a time-to-event modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).

Short-Term Forecasting of Traffic Volume: Evaluating Models Based on Multiple Data Sets and Data Diagnosis Measures

Although several methods for short-term forecasting of traffic volume have recently been developed, the literature lacks studies that focus on how to choose the appropriate prediction method on the basis of the statistical characteristics of the data set. This study first diagnosed the predictability of four traffic volume data sets on the basis of various statistical measures, including (a) complexity analysis methods, such as the delay time and embedding dimension method and the approximate entropy method; (b) nonlinearity analysis methods, such as the time reversibility of surrogate data; and (c) long-range dependency analysis techniques, such as the Hurst exponent. After the data sets were diagnosed, three models for short-term prediction of traffic volume were applied: (a) seasonal autoregressive integrated moving average (SARIMA), (b) k nearest neighbor (k-NN), and (c) support vector regression (SVR). The results from the statistical data diagnosis methods were then correlated to the performance results of the three prediction methods on the four data sets to determine the means for choosing the appropriate prediction method. The results revealed that SVR was more suitable for nonlinear data sets, while SARIMA and k-NN were more appropriate for linear data sets. The data diagnosis results were also used to devise a selection process for the parameters of the prediction models, such as the length of the training data set for SARIMA and SVR, the average number of nearest neighbors for k-NN, and the input vector length for k-NN and SVR.

CASE-BASED REASONING FOR ASSESSING INTELLIGENT TRANSPORTATION SYSTEMS BENEFITS

Current transport planning modeling tools have critical limitations with respect to assessing the benefits of intelligent transportation systems (ITS) deployment. In this paper, a novel framework for developing modeling tools for quantifying ITS deployments benefits is presented. The approach is based on use of case-based reasoning, an artificial intelligence paradigm, to capture and organize the insights gained from running a dynamic traffic assignment (DTA) model. To demonstrate the feasibility of the approach, this study develops a prototype system to evaluate benefits of diverting traffic away from incident locations using variable message signs. A real-world network from the Hartford (Connecticut) area is used in developing the system. Performance of the prototype is evaluated by comparing its predictions to those obtained using a detailed DTA model. The prototype system is shown to yield solutions comparable to those obtained from the DTA model, demonstrating the feasibility of the approach.

Large-scale Agent-based Traffic Micro-simulation: Experiences with Model Refinement, Calibration, Validation and Application

Abstract This paper describes the authors’ continued efforts toward the development, calibration, validation and application of a large-scale, agent-based model of the Buffalo-Niagara metropolitan area. The model is developed using the TRansportation ANalysis SIMulation System (TRANSIMS), an open-source, agent-based suite of transportation models originally developed by Los Alamos National Lab (LANL). Following the network error-checking, calibration and validation phases of the model development cycle, the model was used to evaluate the impact of significant snow storm events on the performance of surface transportation network. This was done by modifying the behavior of the agents (i.e. the drivers) in the model to reflect more conservative driving behavior and vehicle dynamics limitations (such as maximum acceleration and deceleration) imposed by the impaired road surface condition. The study demonstrates that the development of regional agent-based models is technically feasible, but one that requires significant efforts in terms of network accuracy checking, model calibration and validation. Moreover, it is shown that inclement weather events reduce the ability of transportation networks to handle the travel demand, which in turn underscores the importance of effective travel demand management during such events.

Vehicle speed control algorithms for eco-driving

Given the rise in fuel prices and the harmful environmental consequences of excessive fuel consumption, we address a new problem in eco-driving, which examines how the upcoming V2V/V2I technology can be harnessed to improve fuel-efficiency. Unlike most of the existing studies in this area where the focus of control is on infrastructure side (i.e., signal timing plans), we present a new approach to eco-speed control at a microscopic level. We use a concept of platoon of vehicles to reduce fuel consumption in a journey covering multiple intersections in a multiple vehicle setting. Three heuristic algorithms are proposed and numerical results from simulations are also presented.

Computationally-efficient Approaches to Integrating the MOVES Emissions Model with Traffic Simulators

Abstract This paper investigates different approaches to integrating the MOtor Vehicle Emission Simulator model (MOVES) with microscopic traffic simulators to allow for project-level emissions analysis. The integration methodology is based on using the second-by-second vehicle trajectory output from the traffic simulator to define the link drive schedule required to run MOVES. This raises the question of how to define a representative vehicle trajectory for each link, because tracking the emissions for individual vehicles is computationally intractable. In this study, the accuracy of two aggregation methods and one sampling method, for defining the representative trajectory, are compared for both freeway and arterial links. The results indicate that the sampling method outperforms either one of the aggregation methods, and that using as few as five probes can achieve over 95% accuracy in a timely manner.

Safety Effects of Exclusive Left-Turn Lanes at Unsignalized Intersections and Driveways

This article presents an investigation of the safety effects of exclusive left-turn lane installation at unsignalized intersections. Selected intersections were categorized by area type, the number of lanes, and the number of approach legs, because these characteristics could potentially affect the number of crashes as well as the crash type and severity. Crash data were collected from 1995 to 2004 for each intersection and categorized by (1) same-direction crashes, (2) intersecting crashes, and (3) others. Fatal and injury crashes were also collected. Crash prediction models were estimated using crash and volume data from intersections without left-turn lanes by intersection category. Negative binomial modeling was used with generalized estimation equations to account for the correlation among the crash counts for an intersection through the years. The expected number of crashes was predicted using the prediction models for intersections with left-turn lanes assuming no left-turn lanes were present. If the observed crash counts were significantly lower than the predicted, then there was evidence that the left-turn lane created a safer condition. The results showed that the intersections were safer for same-direction crashes and fatal and injury crashes with some exceptions in urban areas.

Special Issue on Cyber Transportation Systems and Connected Vehicle Research

ADEL W. SADEK,1 BYUNGKYU “BRIAN” PARK,2 and MECIT CETIN3 1Department of Civil, Structural, & Environmental Engineering, University at Buffalo, The State University of New York, Buffalo, New York, USA 2Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, Virginia, USA 3Civil & Environmental Engineering Department, Old Dominion University, Norfolk, Virginia, USA