Bekir Bartin

Derivation and Validation of New Simulation-Based Surrogate Safety Measure

Traffic safety evaluation is one of the most important processes in analyzing transportation systems performance. Traditional methods like statistical models and before-after comparisons have many drawbacks, such as limited time periods, sample size problems, and reporting errors. The advancement of traffic conflict techniques combined with microsimulation offers a potentially innovative way for conducting safety assessment of traffic systems even before safety improvements are implemented. In this paper, simulation-based safety studies are reviewed, and a modified simulation-based surrogate safety measure and a new simulation-based surrogate safety measure that can capture the probability of collisions, as well as the severity of these potential collisions, are proposed. Conceptual and computational logic of the proposed surrogate safety indicators are described in detail. These surrogate safety indices are initially proposed for link-based analysis and should not be used for other purposes, such as intersection safety assessment, without further enhancements, and the use of these indices should be limited to the analysis of linear conflicts. In addition, these link-based indices are extended to be able to conduct aggregate networkwide safety assessments. The proposed indices are validated by means of a well-calibrated traffic simulation model of a section of the New Jersey Turnpike and real accident data from the same section. Preliminary results indicate a strong relationship between the proposed surrogate safety measures and real accident data. Further research is needed to investigate these new surrogate safety indices under different locations and traffic conditions.

Clustering-Based Methodology for Determining Optimal Roadway Configuration of Detectors for Travel Time Estimation

The problem of finding the optimal roadway segment configuration for road-based surveillance technologies to estimate route travel times accurately is addressed. This problem is inherently a space discretization problem regardless of which travel time estimation function is used. Its ad hoc solution is the equidistant segment configuration, such as every half mile or every 1 mi. It is shown that the space discretization problem can be expressed as the common clustering problem. The novelty of the proposed approach is the use of preliminary vehicle trajectory data to obtain statistically significant traffic regime at the study route. Clustering of sample space-time trajectory data is proposed as a viable methodology for solving the optimal roadway segment configuration problem.

Impact of Electronic Toll Collection on Air Pollution Levels: Estimation Using Microscopic Simulation Model of Large-Scale Transportation Network

This paper presents a microscopic simulation-based estimation of the spatiotemporal change in air pollution levels as a result of electronic toll collection (ETC) deployment on the New Jersey Turnpike (NJTPK), a large-scale traffic network. The study includes (a) the disaggregate spatial estimation and analysis of the emissions instead of aggregate systemwide estimations, (b) the use of a vehicle-based and well-calibrated traffic simulation model of NJTPK network in Paramics microscopic simulation software to perform this disaggregate emission estimation, (c) the use of a unique and realistic toll plaza model with a complete mainline model to capture complex mainline–toll plaza interactions, and (d) estimation of short- and long-term impacts of ETC systems. The simulation model is loaded with a recent network-specific data set, which includes origin–destination demand data for 1999 (before ETC deployment) and for 2005 (6 years after ETC deployment) and toll plaza service times obtained from toll plaza videotapes. The MOBILE6.2 mobile source emission model developed by the Environmental Protection Agency is integrated in Paramics. At each time step of the simulation, air pollution levels—namely, CO, HC, NOx, and PM10 emissions—are calculated for each vehicle type on the basis of its speed. The simulation network is used to estimate not only the change in systemwide air pollution levels but also the spatial changes throughout the system. Results show that ETC deployment reduces the overall network air pollution level in the short term; however, in the long term its benefits are not sufficient to compensate for the air pollution increase on the mainline because of annual traffic growth.

Mining the Characteristics of Secondary Crashes on Highways

The prevention of secondary crashes is a high priority task in traffic incident management. However, the limited knowledge regarding the nature of secondary crashes largely impeded the development of established countermeasures. The primary goal of this paper is to improve the literature’s understanding of secondary crashes. This goal is achieved in two steps: first, with an analysis framework that accurately identifies secondary crashes by integrating rich traffic-sensor data with statewide-crash data and, second, by carefully investigating the characteristics of these identified secondary crashes. To that end, secondary crashes within a 27-mile section of a major highway in New Jersey were mined using the developed analysis framework, and a thorough examination of their characteristics has been performed. Empirical findings on the frequency of secondary crashes, their spatio-temporal distributions, clearance time, crash type, severity, and major contributing factors have been highlighted. Taken together, these preliminary results could potentially help transportation agencies make more informed decisions on mitigating secondary crashes and improve their incident management operations. To complement the results, further in-depth investigations using more high-resolution sensor data and high-quality incident records are suggested.

Determining the Optimal Configuration of Highway Routes for Real-Time Traffic Information: A Case Study

This paper deals with a case study where the objective is to identify the optimal subset of routes for real-time traveler information in a highway network. It is assumed that the benefit of providing this information is directly related to the uncertainty of route travel times. The variance of travel times within a time period over consecutive days is employed as the indicator of this uncertainty. The New Jersey Turnpike is used as the study network due to the availability of vehicle-by-vehicle network-specific data. The data set covers travel times between ~ 630 origin-destination (OD) pairs during 2004. The problem of identifying the optimal number of subset of routes is modeled as a nonlinear integer-programming problem. The proposed model is then solved using the Network-Enabled Optimization Software server, which is a common optimization solver that is available over the Internet. A simple heuristic for the proposed model is also presented.

South Jersey Real-Time Motorist Information System: Technology and Practice

The South Jersey Real-Time Motorist Information System project is aimed at the rapid deployment of available intelligent transportation system surveillance and communication technologies to monitor traffic on the basis of need at various locations in the South Jersey highway network. The proposed system is a highly mobile traffic-surveillance design that includes mobile, self-sufficient, sensor units with communications and data-collection capabilities that allow the sensor unit to exchange information with the traffic-control center. The sensor units can be installed easily at any location on the transportation network without delays for establishing power and communication connections. This rapid-deployment capability meets the goal for easy-to-deploy traffic-surveillance units that can be used to manage the transportation system during any type of disaster. A technical overview and the advantages of this system and its evaluation procedure are presented. In addition, the problems faced and lessons learned during the implementation, along with future plans for deployment, are presented.

A Clustering Based Methodology for Determining the Optimal Roadway Configuration of Detectors for Travel Time Estimation

This paper deals with the problem of finding the optimal roadway segment configuration for road-based surveillance technologies to estimate route travel times accurately. This problem is inherently a space discretization problem regardless of which travel time estimation function is used. The ad-hoc solution to this problem is the equidistant segment configuration, such as every half-mile, every one-mile. It is shown in this paper that the space discretization problem can be expressed as the common clustering problem. The novelty of the proposed approach is the use of preliminary vehicle trajectory data to obtain statistically significant traffic regime at the study route. Clustering of sample space-time trajectory data is proposed as a viable methodology for solving the optimal roadway segment configuration problem

Evaluation of incident management strategies and technologies using an integrated traffic/incident management simulation

This paper describes Rutgers Incident Management System (RIMS) software that is developed to evaluate the benefits of various incident management strategies and technologies. This tool can generate incidents and test various response strategies and technologies. South Jersey highway network is used as a test network due to the available historical incident data. The evaluated incident management strategies include the deployment of Variable Message Signs (VMS) to divert traffic during incidents and the use of Freeway Service Patrols (FSPs) for detecting and verifying incidents efficiently. The simulation-based evaluations also include the effect of cellular phone users in the network on the incident detection and verification times. The results show that the studied incident management strategies have positive impacts on reducing incident durations while being cost effective. More specifically, the deployment of VMS for diverting traffic in case of an incident results in a benefit cost ratio of 9.2:1; an additional service unit in freeway patrol results in reduced incident detection and verification time with a corresponding benefit-cost ratio of 3.9:1.

Use of sensor data to identify secondary crashes on freeways

Nonrecurring traffic incidents, such as motor vehicle crashes, increase not only travel delays but also the risk of secondary crashes. Secondary crashes can cause additional traffic delays and reduce safety. Implementation of effective countermeasures to prevent or reduce secondary crashes requires that their characteristics be investigated. However, the related research has been limited, largely because of the lack of detailed incident and traffic data necessary to identify secondary crashes. Existing approaches, such as static methods employed to identify secondary crashes, cannot fully capture potential secondary crashes because of fixed spatiotemporal identification criteria. Improved approaches are needed to categorize secondary crashes accurately for further analysis. This paper develops an enhanced approach for identifying secondary crashes that uses the existing crash database and archived traffic data from highway sensors. The proposed method is threefold: (a) defining secondary crashes, (b) examining the impact range of primary crashes that possibly relate to secondary crashes, and (c) identifying secondary crashes. The proposed methodology establishes a practical framework for mining secondary crashes from existing sensor data and crash records. A case study was performed on a 27-mi segment of a major highway in New Jersey to illustrate the performance of the proposed approach. The results show that the proposed method provides a more reliable and efficient categorization of secondary crashes than commonly used approaches.

Empirical Evacuation Response Curve During Hurricane Irene in Cape May County, New Jersey

Understanding evacuation response behavior is critical for public officials in deciding when to issue emergency evacuation orders for an impending hurricane. Such behavior is typically measured by an evacuation response curve that represents the proportion of total evacuation demand over time. This study analyzes evacuation behavior and constructs an evacuation response curve on the basis of traffic data collected during Hurricane Irene in 2011 in Cape May County, New Jersey. The evacuation response curve follows a general S-shape with sharp upward changes in slope after the issuance of mandatory evacuation notices. These changes in slope represent quick response behavior, which may be caused in part by an easily mobilized tourist population, lack of hurricane evacuation experience, or the nature of the location, in this case a rural area with limited evacuation routes. Moreover, the widely used S-curves with different mathematical functions and the state-of-the-art behavior models are calibrated and compared with empirical data. The results show that the calibrated S-curves with logit and Rayleigh functions fit empirical data better. The evacuation behavior analysis and calibrated evacuation response models from this hurricane evacuation event may benefit evacuation planning in similar areas. In addition, traffic data used in this study may also be valuable for the comparative analysis of traffic patterns between the evacuation periods and regular weekdays and weekends.