Identifying Wrong-Way Driving Hotspots by Modeling Crash Risk and Assessing Duration of Wrong-Way Driving Events

Because wrong-way driving (WWD) crashes are often severe, it is important for transportation agencies to identify WWD hotspot segments appropriate for potential implementation of advanced WWD countermeasures. Two approaches to identify these hotspot segments were developed and applied to a case study of limited-access highways in Central Florida. The first approach used a Poisson regression model that predicted the number of WWD crashes in a roadway segment based on WWD citations, 911 calls, traffic volumes, and interchange designs. Combining this predicted crash value with the actual number of WWD crashes in the segment gave the WWD crash risk of the segment. Ranking roadway segments by WWD crash risk provided agencies with an understanding of which segments had high WWD crash frequencies and high potential for future WWD crashes. This approach was previously applied to South Florida; the research presented here extended this approach to Central Florida. The second approach was based on operational data collected in traffic management centers and could be used if accurate WWD 911 and citation data with GPS location were not available or as a supplement to the first approach. The approach identified and ranked WWD hotspots on the basis of the reported duration of WWD events. In Central Florida, the results of the two approaches agreed with each other and were used by agencies to decide where to implement advanced WWD countermeasures. Together, these approaches can help transportation agencies determine regional WWD hotspots and cooperate to implement advanced WWD countermeasures at these locations

Freeway incident detection using Fuzzy ART

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike backpropagation models, Fuzzy ART is capable of fast stable learning of recognition categories. It is an incremental approach that has the potential for online implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-second loop detector data of occupancy, speed, or a combination of both. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 minutes was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-second periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than occupancy patterns. However, when combined in one pattern, occupancy and speed patterns yield the best results with 100% detection rate and 0.07% false alarm rate

Truck Trip Generation Models For Seaports With Container And Trailer Operation

Freight movement throughout the United States continues to evolve as a significant challenge to the transportation industry. Seaport operations dominated by container and trailer movements will require operational and infrastructure changes to maintain the growth of international cargo operations. Transportation planning models can be used to determine the needs of port and street network modifications. Described is the research and initial development process of models for predicting the levels of cargo truck traffic moving inbound and outbound at the Port of Miami. The models were restricted to container and trailer truck configurations that transport virtually all of the Port of Miami\u27s freight. Consequently, this associated truck traffic moves through the nearby street network within downtown Miami. The purpose of the trip generation models is to predict volumes of large inbound and outbound trucks for specified time frames. The concern is to know how many large cargo vehicles are traveling on the only road leading to the port. Primary factors affecting truck volume were found to be the amount and direction of cargo vessel freight and the particular weekday of operation. Time series models for predicting seasonal variations in freight movements were developed as part of the study. These models are useful for long-term forecasts of the input variables used in the trip generation models. Truck trip generation models will provide transportation planners and public agencies with valuable information when making transportation management decisions and infrastructure modifications. This information also is necessary for prioritizing funds for roadway upgrade projects

Traffic operations during Electronic Toll Collection: Case study of the Holland East Plaza

The toll plaza model or TPModel is a macroscopic analytical queuing model, developed for estimating the rush hour delay at toll collection facilities. TPModel can be used to compare the performance of a plaza under different lane configurations. A delay sensitivity analysis was performed on each of the model\u27s variable. TPModel estimated increasing delay with increasing approach traffic rush hour volumes. TPModel also estimated smaller plaza delays as the service rate for the various services increased

Applications of queuing models to Electronic Toll Collection

Electronic Toll Collection (ETC) via Automatic Vehicle Identification (AVI) technology has significantly altered traffic operations during toll collection. In particular, the value of the average processing rate of a lane providing both ETC service as well as a traditional service, fluctuates over the rush hour between the average value of the processing rate of the traditional service and the capacity of the ETC service. This study develops a queuing model to address the changing processing rates for the different mixed lanes. The model is applied to the westbound 9-lane portion of the Holland East Plaza in Orlando, Florida. Data is evaluated for 6 different rush hours that include 3 different configuration patterns implemented over a period of 3 years. In the first configuration, only the traditional toll collection services are provided. In another configuration, all traditional lanes become mixed to include ETC except for the center lane, which becomes a lane dedicated solely to ETC service. In a final configuration, two lanes become dedicated to ETC service. A calibration factor between 1.15 and 1.19 corrects the predicted delay. A plaza delay sensitivity analysis is performed on each of the model\u27s input variables

Modeling traffic operations at electronic toll collection and traffic management systems

Automatic Vehicle Identification (AVI) technology is one possible solution to traffic congestion at existing transportation facilities. This paper presents a mathematical model of traffic conditions for toll plaza facilities that includes AVI toll collection services among other conventional services. Three types of services are available: manual toll service, in which transactions are handled by a toll collector, automatic toll service, in which coin machines are utilized, and AVI toll collection service. In addition, mixed lanes, which provide more than one of the above services, are considered by the model. For a given rush hour, queue lengths and delays can be calculated for different toll plaza configurations. Comparison of their performance may aid operators in the management of the lane configurations until all users of the facility become AVI patrons. © 1997 Elsevier Science Ltd