Automatic Vehicle Identification Technology Research Articles

Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile

Nowadays, freight transport is crucial in the functioning of cities worldwide. To dig further into the understanding of urban freight transport movements, in this research, we conducted a case study in which we estimated an origin-destination matrix for the trucks traveling on Autopista Central, one of Santiago de Chile’s most important urban highways. To do so, we used full real-world vehicle-by-vehicle information of freight vehicles’ movements along the highway. This data was collected from several toll collection gates equipped with free-flow and automatic vehicle identification technology. However, this data did not include any vehicle information before or after using the highway. To estimate the origins and destinations of these trips, we proposed a multisource methodology that used GPS information provided by SimpliRoute, a Chilean routing company. Nevertheless, this GPS data involved only a small subset of trucks that used the highway. In order to reduce the bias, we built a decision tree model for estimating the trips’ origin, whose input data was complemented by other public databases. Furthermore, we computed trip destinations using proportionality factors obtained from SimpliRoute data. Our results showed that most of the estimated origins belonged to outskirt municipalities, while the estimated destinations were mainly located in the downtown area. Our findings might help improve freight transport comprehension in the city, enabling the implementation of focused transport policies and investments to help mitigate negative externalities, such as congestion and pollution.

Assessing influential factors for lane change behavior using full real-world vehicle-by-vehicle data

ABSTRACT Understanding the underlying reasons for potential human risky driving behaviors is crucial for improving road safety. Recent technologies allow the analysis of driving behaviors at a microscopic level, allowing a naturalistic observation of such phenomenon through information collected unobtrusively. This paper assesses the factors that influence discretionary lane changes on an urban highway in Santiago, Chile, employing an interpretable machine learning approach. We use full real-world vehicle-by-vehicle data gathered from Automatic Vehicle Identification technology and individual public information of the vehicles and their owners, which allows us to understand patterns that might have different characteristics when performed in simulated environments. We provide insights about the causes that increase the likelihood of lane changes. For example, we find that: (i) the older the car, the less likely it is to change lane, (ii) younger drivers change lane more often, and (iii) motorcycles drivers are the most likely to change lane.

Real-Time Prediction of Arterial Roadway Travel Times Using Data Collected by Bluetooth Detectors

Bluetooth detectors are gaining in popularity as a cost-effective technology for acquiring travel time data. The sensors, which identify and record the unique media access control address of Bluetooth-enabled devices, can measure travel time when a device passes through the detection zone of two consecutive Bluetooth detectors. As with other automatic vehicle identification technologies (e.g., toll tags, automatic license plate recognition systems), there is a time lag because the travel time cannot be acquired until the vehicle has passed the downstream detector location. The increasing desire for accurate and timely traveler information and the desire for proactive traffic control present a need for accurate prediction of near-future travel times along roadway corridors. A significant body of literature has focused on this problem for freeways, but little effort has been directed toward signalized arterials. This paper presents a data-driven model for predicting near-future travel times on signalized arterials in real time by using data acquired from Bluetooth detectors. The model uses the k nearest neighbor pattern recognition technique to identify historical data from which an understanding of the near-future traffic patterns can be extracted. Unlike previous efforts, an objective approach was used to determine the variables to include in the k nearest neighbor feature vector and the optimal model parameters. The calibrated model was evaluated through application to a set of field data obtained from Bluetooth detectors deployed on a signalized arterial. The model provides performance improvements of approximately 20% over a benchmark model.

Application of automatic vehicle identification technology for real-time journey time estimation

This paper presents a real-time traveler information system (RTIS) for journey time estimation using automatic vehicle identification (AVI) data in Hong Kong. This RTIS can also deduce the travel times on the other road links without real-time AVI data. The travel times, in RTIS, are estimated by the real-time AVI data, the off-line travel time estimates and the related spatial variance–covariance relationships between road links. The real-time AVI data adopted for RTIS are Autotoll tag data in Hong Kong; whereas the off-line link travel time estimates and their spatial variance–covariance matrices are obtained from a traffic flow simulator. By integration of these real-time and off-line traffic data together, the travel times on the road links either with or without real-time AVI data can be updated at 5-min intervals. Validation of journey time estimates is carried out at various selected paths in Hong Kong urban area. The results show that the performance of RTIS on real-time journey time estimation in Hong Kong is satisfactory and acceptable.

Automatic Vehicle Identification Technology-Based Freeway Incident Detection

The recent emergence of automatic vehicle identification technology (AVI) for use in electronic toll collection has provided an opportunity to develop automatic incident detection (AID) methods that rely on individual vehicle travel time data instead of loop detector data. The performances of three AVI-based AID algorithms are examined. Travel time data for testing of the algorithms were obtained by simulating a 12-km section of the collector facility of Highway 401 in Toronto, Ontario, Canada. The performances of the three AVI-based AID algorithms are compared with the performance of a leading loop detector–based algorithm, which was independently tested with similar simulated data. The AID performance results indicate that AVI-based AID algorithms can provide incident detection performance similar to that of existing loop detector-based AID methods.

Operational Benefits of Electronic Toll Collection: Case Study

This paper reports the improvements in traffic operations at the electronic toll collection plazas of the Orlando-Orange County Expressway Authority. Service time, vehicle arrival times, and departure times, as well as vehicle counts were collected “before” and “after” the installation of automatic vehicle identification technology known as E-PASS. The findings indicate that, for the dedicated E-PASS lane, the measured capacity has tripled, the service time has decreased by five seconds per vehicle, the average queuing delay has decreased by one minute per vehicle, the maximum queuing delay has decreased by 2.5–3 minutes per vehicle, and the total queuing delay has decreased by 8.5–9.5 vehicle-hours per morning peak hour for that lane. Also, variability in the headway has been reduced significantly in the dedicated E-PASS lane. Capacity, headway, and service times of the mixed (manual/E-PASS or automatic/E-PASS) lanes did not change significantly. However, arrivals have shifted to the dedicated E-PASS lan...

Automatic Vehicle Identification Technology Applications to Toll Collection Services

Intelligent transportation systems technologies are being developed and applied through transportation systems in the United States. An example of this type of innovation can be seen on toll roads where a driver is required to deposit a toll in order to drive on a toll road. To automatically process toll services, automatic vehicle identification (AVI) technologies should be developed and implemented. A summary is presented of a study that focused on the performance analysis of three types of AVI technologies that could be used for toll collection applications on the Crosstown Expressway and the Veterans Expressway in Hillsborough County, Florida. The three AVI technology types were optical/laser scanner, radio frequency, and inductive loop. The study aimed at analyzing the total average delay and AVI market share due to the usage of AVI technologies. Delay performance and market share resulting from the application of each AVI technology type were used to compare the effects of different AVI technology types. The analysis was based on computer simulation using an AVI lane choice model (demand model) and a delay model (performance model) with the data collected from the two toll roads in Hillsborough County. From the simulation results, a cost-benefit analysis was performed to evaluate the impacts of AVI technologies on the ratio of benefit over cost. Results of the cost-benefit analysis can be used to determine the optimal configuration of AVI lanes and manual lanes. A procedure is provided that can be used for planning and designing AVI technologies used in toll service.

Short-Term Improvements To Increase Capacity of Highly Congested Mainline Toll Plazas in Puerto Rico

In 1993 the Department of Transportation and Public Works of Puerto Rico (DTPW) implemented toll gates at expressway toll collection plazas because of significant losses in revenue caused by failure of motorists to pay the established fee. However, it was presumed that the use of toll gates as an enforcement device for effective traffic control might create additional delays, particularly during peak hours in already highly congested toll plazas near metropolitan areas. The lack of additional open space to expand the existing toll facilities combined with the negative public image associated with high levels of congestion, attributed to implementation of the toll gates, triggered the need to explore cost-effective, short-term alternative solutions. DTPW efforts to address the issues of congestion and negative public image in the highly congested toll facilities are described. A series of experimental studies were performed to assess the effectiveness of toll gates and automatic vehicle identification (AVI) technology in increasing the capacity of low-volume and highly congested toll facilities. The findings were analyzed for significant differences in delays and for potential changes in driver behavior that could assist in mitigating delays at the toll plazas. The results of the first study revealed that the implementation of toll gates creates additional delays for users. The second study found that AVI technology is fast, efficient, and reliable in the detection of vehicles when used in low-volume toll plazas; however, it is not efficient in highly congested toll plazas because of the additional delays generated from the queue. In the third study it was demonstrated that the use of ushers to collect coins from drivers and deposit them in the toll basket resulted in a significant increase in capacity at toll plaza stations, time savings, and fuel economy for users.

Applications of automatic vehicle identification technology

There are several approaches to automatic vehicle identification (AVI) which have been developed since the first investigations of AVI in the 1960s. Recent significant advances in vehicle detection and data processing techniques have made the application of AVI systems both technically and economically feasible. This paper is concerned with the application of AVI technology to the transport environment and examples of three different applications are provided. Following an overview of the technology and uses of AVI, the breadth of transport applications is covered. Two specific applications in the areas of fleet monitoring and control and of automatic toll collection are described. The first is the US Heavy Electronic Licence Plate (HELP) programme: a major research and development effort aimed at investigating and developing an integrated heavy vehicle monitoring and management system. The system combines AVI with weigh‐in‐motion and automatic vehicle classification technologies and a communications and ...