Borman Expressway Research Articles

Simulation study of the impact of local real-time traffic information provision strategy in connected vehicle systems

Abstract Current vehicle navigation systems usually rely on global real-time traffic information. They suggest the best paths to travelers through an independent and selfish-routing mechanism. Existing studies show that such systems have a great potential to cause traffic congestions oscillating among several alternative corridors, i.e., iteratively leading exceeding traffic into light traffic corridors and making congestion on one and then another within a short time period. To address this issue, this research proposes a local information provision strategy, given travelers are still guided by an independent and selfish-routing mechanism. The coverage of the real-time information provision is identified by the minimum bounding rectangle, and the ellipse method respectively. The benefits of the proposed strategy in system level are investigated by the simulation studies built upon the Borman expressway network. The simulation results indicate that as the local travel time information provision strategy is used, it may save 10–25% of the system travel time resulting from global information provision strategy under medium or high network traffic loads. Moreover, the sensitivity analyses indicate that the merits of the local travel time information provision strategy are more significant as the penetration of smart vehicles increases over the network. Thus, the proposed local information provision strategy outperforms global information provision strategy in system level network performance.

Model for Forecasting Expressway Fine Particulate Matter and Carbon Monoxide Concentration: Application of Regression and Neural Network Models

The Borman Expressway is a heavily traveled 16-mi segment of the Interstate 80/94 freeway through Northwestern Indiana. The Lake and Porter counties through which this expressway passes are designated as particulate matter <2.5 µm (PM2.5) and ozone 8-hr standard nonattainment areas. The Purdue University air quality group has been collecting PM2.5, carbon monoxide (CO), wind speed, wind direction, pressure, and temperature data since September 1999. In this work, regression and neural network models were developed for forecasting hourly PM2.5 and CO concentrations. Time series of PM2.5 and CO concen trations, traffic data, and meteorological parameters were used for developing the neural network and regression models. The models were compared using a number of statistical quality indicators. Both models had reasonable accuracy in predicting hourly PM2.5 concentration with coefficient of determination ~0.80, root mean square error (RMSE) <4 µg/m3, and index of agreement (IA) >0.90. For CO prediction, both models showed moderate forecasting performance with a coefficient of determination ~0.55, RMSE ~0.50 ppm, and IA ~0.85. These models are computationally less cumbersome and require less number of predictors as compared with the deterministic models. The availability of real time PM2.5 and CO forecasts will help highway managers to identify air pollution episodic events beforehand and to determine mitigation strategies.

Development of an Empirical Model to Estimate Real-World Fine Particulate Matter Emission Factors: The Traffic Air Quality Model

The purpose of the study was to quantify the impact of traffic conditions, such as free flow and congestion, on local air quality. The Borman Expressway (I-80/94) in Northwest Indiana is considered a test bed for this research because of the high volume of class 9 truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve traffic management along the highway stretch. An empirical traffic air quality (TAQ) model was developed to estimate the fine particulate matter (PM2.5) emission factors (grams per kilometer) based solely on the measured traffic parameters, namely, average speed, average acceleration, and class 9 truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the U.S. Environmental Protection Agency (EPA)-PART5 model. During congestion (defined as flow-speeds <50 km/hr [30 mi/hr]), the TAQ model, on average, overpredicted the measured values only by a factor of 1.2, in comparison to a fourfold underprediction using the EPA-PART5 model. On the other hand, during free flow (defined as flow-speeds >80 km/hr [50 mi/hr]), the TAQ model was conservative in that it overpredicted the measured values by 1.5-fold.

Modeling and Mitigation of Car-Truck Interactions on Freeways

Trucks represent the predominant mode of domestic freight movement. Because of the substantial increase in freight truck traffic on the nation’s highways, its influence on traffic flow performance, safety, and the quality of the travel experience is receiving increased attention. The behavior of non–truck drivers is modeled in terms of their interactions with trucks in the traffic stream; existing microscopic freeway traffic flow modeling logic is extended to incorporate these interactions; and alternative strategies to mitigate them are evaluated. The car–truck interactions are modeled by associating a “discomfort level” for every non–truck driver in the vicinity of trucks. This discomfort is affected by driver socioeconomic characteristics and situational factors such as time of day, weather, and ambient traffic congestion levels. Stated-preference surveys of non–truck drivers were used to elicit the factors that influence their behavior when they interact with trucks on highways. A fuzzy logic–based model was used to determine non–truck driver discomfort level. The model characterizes non–truck driver behavior near trucks by using if-then rules constructed with the survey data. The discomfort level is used in conjunction with the car-following and lane-changing logics of a traditional traffic flow model to generate a truck-following model and a corresponding lane-changing model. This redresses a key methodological gap in the literature and provides a capability to analyze alternative strategies to mitigate car–truck interactions. An agent-based freeway segment traffic flow simulator was constructed by using these extended microscopic flow models. Simulation experiments with data from the Borman Expressway (I-80 and I-94) in northwest Indiana were used to analyze the sensitivity of the model to the various parameters and evaluate the effectiveness of alternative mitigation strategies.

Incident Occurrence Models for Freeway Incident Management

Incident prediction models are presented for the Interstate 80/Interstate 94 (Borman Expressway in northwestern Indiana) and Interstate 465 (northeastern Indianapolis, Indiana) freeway sections developed as a function of traffic volume, truck percentage, and weather. Separate models were developed for all incidents and noncrash incidents. Three model types were considered (Poisson regression, negative binomial regression, and nonlinear regression), and the results were compared based on magnitudes and signs of model parameter estimates and t-statistics. Least-squares estimation and maximum-likelihood methods were used to estimate the model parameters. Data from the Indiana Department of Transportation and the Indiana Climatology Database were used to establish the relationships. For a given session and incident category, the results from the Poisson and negative binomial models were found to be consistent. It was observed that, unlike section length, traffic volume is nonlinearly related to incidents, and therefore these two variables have to be considered as separate terms in the modeling process. Truck percentage was found to be a statistically significant factor affecting incident occurrence. It was also found that the weather variable (rain and snow) was negatively correlated to incidents. The freeway incident models developed constitute a useful decision support tool for implementation of new freeway patrol systems or for expansion of existing ones. They are also useful for simulating incident occurrences with a view to identifying elements of cost-effective freeway patrol strategies (patrol deployment policies, fleet size, crew size, and beat routes).

Real-Time Variable Message Sign–Based Route Guidance Consistent with Driver Behavior

The variable message sign (VMS) represents a cost-effective mechanism for disseminating information to drivers unequipped to receive personalized information. The VMS can be used during incidents to divert traffic to less congested areas of the network and circumvent lengthy queues, better utilize network capacity, and improve system performance. A VMS control heuristic framework is proposed and evaluated that seeks diversion during incidents to enable a traffic system controller to favorably manage traffic conditions in real time. The framework ensures consistency with driver diversion response behavior, is responsive to changing traffic conditions, enables computational tractability through stage-based online implementation, and ensures the spatial and temporal consistency of the displayed messages. A hybrid framework is used that consists of offline and online components to determine the information for the VMS. The ability to display messages consistent with driver diversion behavior represents a valuable tool for the controller to enhance system effectiveness by simultaneously satisfying systemwide and individual user objectives. Data on driver VMS response attitudes from the Borman Expressway corridor in northwestern Indiana and simulated experiments are used to derive insights into the practical effectiveness of the proposed VMS control heuristic.

Evaluation of Mobility Impacts of Advanced Information Systems

Advanced technologies under the aegis of advanced traveler information systems and advanced traffic management systems are being employed to address the debilitating traffic congestion problem. Broadly identified under the label intelligent transportation systems (ITS), they focus on enhancing the efficiency of the existing roadway utilization. Though ITS has transitioned from the conceptual framework stage to the operational test phase that analyzes real-world feasibility, studies that systematically quantify the multidimensional real-world impacts of these technologies in terms of mobility, safety, and air quality, are lacking. This paper proposes a simulation-based framework to address the mobility impacts of these technologies through the provision of information to travelers. The information provision technologies are labeled as advanced information systems (AIS), and include pretrip information, en route information, variable message signs, and combinations thereof. The primary focus of the paper is to evaluate alternative AIS technologies using the heavily traveled Borman Expressway corridor in northwestern Indiana as a case study. Simulation results provide insights into the mobility impacts of AIS technologies, and contrast the effectiveness of alternative information provision sources and strategies.

ITS Impacts on Safety and Traffic Management: An Investigation of Secondary Crash Causes

Freeway service patrols, an increasingly popular choice in combating the effects of traffic incidents on freeway operations, have been known to reduce incident response and clearance time. This reduction can help alleviate the delay due to non-recurrent, incident related congestion, as well as lower the chance of secondary crashes. While much work has been done in the direction of identifying the benefits stemming from the delay savings by freeway patrols, little has been done to identify the potential savings from lowering the likelihood of secondary crash occurrence. Using a 5-year incident data base from the Borman Expressway, we develop logistic regression models to examine what primary crash characteristics are likely to influence the likelihood of a secondary crash. The findings suggest that clearance time, season, type of vehicle involved, and lateral location of the primary crash significantly influence the likelihood of secondary crash occurrence. Further, the 1995 potential benefit from secondary crash reduction was $568,080 exceeding the 1995 Hoosier Helper freeway service patrol program costs by a factor of 1.38. Given a better understanding of what contributes to secondary crash occurrence, various components of incident management can be operated at a higher level of cost-effectiveness.