Automated Vehicle Location Systems Research Articles

Does Crowding Have a More Complicated Effect on Public Transport Users with Respect to Perceived Travel Time?

The issue of crowding in public transportation has long been of concern. Numerous studies have been conducted to address this issue. It has become increasingly common to convert crowding to something more tangible, such as cost and time, in recent years. As a result of converting crowding into time, the concept of perceived travel time (PTT) has been introduced. Viewing crowding from the perspective of PTT may provide new insights into crowding and its effects. Therefore, in this study the PTT was calculated using the data obtained from both automated fare collection and automated vehicle location systems for the most heavily populated bus rapid transit (BRT) line in Tehran, the capital of Iran. A total of 429,000 PTTs were calculated, and then cluster analysis was used to determine that crowding has a significant effect on the PTT, not only for those who travel during peak hours, but also for those who travel during non-peak hours. Crowding therefore appears to have a more complex effect that persists over time. We conducted a time series analysis for some selected origin stops to test this theory. For stops intersected by other BRT lines or metro lines, the results suggest that the difference between PTT and nominal travel time for each block (stop-to-stop distance) increases over time, despite the start travel time not being chosen during peak hours for these stops. Based on the findings of our study, crowding could even affect travelers traveling during low-demand hours if viewed from the point of view of PTT. This effect is referred to as the “dynamic effect of crowding” in this paper.

Performance analysis of GNSS units in manned helicopter operations

Global Navigation Satellite System (GNSS) receivers make use of the United States' Global Positioning System (GPS), Russia's Global Navigation Satellite System (GLONASS), Europe's Galileo, China's Beidou, and Japan's Quasi-Zenith Satellite System constellations to determine the geolocation of the receiver based on the location of satellites and measurements of the distance and travel time of the GNSS signals. GNSS signals are dependent on line of sight to satellites with reflections or obstructions resulting in errors in the estimated position [1]. GNSS systems, while having their origin in military applications [2], have been adopted for many civilian uses from recreational navigation [3] to automated vehicle location systems [4]. While the GPS Standard Positioning Service without any correction sources establishes a 30 m range error limit for civilian use, GPS receivers typically perform better than this standard with errors primarily from atmospheric interference [5]. This is further improved in mobile devices by assisted GPS, WiFi, and cellular positioning when available [6].

Identifying and Visualizing Congestion Bottlenecks with Automated Vehicle Location Systems

Monitoring speeds and identifying problem areas are essential for any public transport system because of the direct impact on its operating costs and on users’ travel time. This study generated a tool that identified, quantified, and displayed operational bottlenecks of bus operation in a city. The model was applied to the public transport system in Santiago, Chile, which faced a steady decline in operating speed. It was possible to identify locations with the most serious problems; this factor allowed transit authorities to focus their efforts on the areas that needed it the most. In addition, it was found that problems were concentrated in certain sectors of the city, including the central business district and intersections where the radial axis roads encountered the city’s central ring road. Once a problem is identified, it is essential to conduct site visits and combine the findings of this research with other sources of information to find the cause of the problem and propose solutions.

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The description and the construction of the generalized potential function with cylindrical surfaces on traffic lanes where an automated vehicle is to pass taking into account dimensional circle has been performed using the proposed algorithm and the programme. It has been determined that these results can be used while designing automated vehicle location system and mechanical accounting system and placing containers in companies specializing in transport operations with containers of general and special purpose.

Development of a Data-Driven Platform for Transit Performance Measures Using Smart Card and GPS Data

To improve customer satisfaction and reduce operation costs, transit authorities have been striving to monitor transit service quality and identify the key factors to enhance it. The recent advent of passive data collection technologies, e.g., automated fare collection (AFC) and automated vehicle location (AVL), has shifted a data-poor environment to a data-rich environment and offered opportunities for transit agencies to conduct comprehensive transit system performance measures. However, most AFC and AVL systems are not designed for transit performance measures, implying that additional data processing and visualization procedures are needed to improve both data usability and accessibility. This study attempts to develop a data-driven platform for online transit performance monitoring. The primary data sources come from the AFC and AVL systems in Beijing, where a passenger’s boarding stop (origin) and alighting stop (destination) on a flat-rate bus are not recorded. The individual transit rider’s origin and destination can be estimated by utilizing a series of data-mining techniques, which are then incorporated into a regional-map platform for transit performance measures. A multilevel framework is proposed to calculate the network-level speed, route-level travel time reliability, stop-level ridership, and headway variance. These statistics are interactively displayed on a map through a simplified transit GIS data model. This platform not only serves as a data-rich visualization platform to monitor transit network performance for planning and operations, it also intends to take advantage of e-science initiative for data-driven transportation research and applications.

Analyzing transit service reliability using detailed data from automatic vehicular locator systems

AbstractThe widespread adoption of automated vehicle location (AVL) systems and automatic passenger counters (APCs) in the transit industry has opened new venues in operations and system monitoring. In 2005, Metro Transit, Minnesota, implemented AVL system and partially implemented APC technologies. To date there has been little effort to employ the collected data in evaluating transit performance. This research uses such data to assess performance issues along a cross‐town route in the Metro Transit system. We generate a series of visual and analytical analyses to predict run time, schedule adherence and reliability of the transit route at two scales: the time point segment and the route level to demonstrate ways of identifying causes of decline in reliability levels. The analytical models show that while headways are maintained, schedule revisions are needed to improve run time and schedule adherence. Finally, the analysis suggests that many scheduled stops along this route are underutilized and recommends stop consolidation as a tool to decrease variability of service through concentrating passenger demand along a fewer number of stops. Copyright © 2010 John Wiley & Sons, Ltd.

Next-Generation Automated Vehicle Location Systems: Positioning at the Lane Level

The majority of today's automated vehicle location (AVL) systems use Global Positioning System (GPS) technology, which can provide position information with an accuracy of approximately 15 m. Recently, low-cost Differential GPS (DGPS) receivers, which have a positioning accuracy of approximate 2-3 m, have become available. With this increased accuracy, it is now possible to perform AVL down to specific roadway lanes. In this paper, a vehicle-lane-determining system is described, consisting of an onboard DGPS receiver that is connected with a wireless communications channel, a unique lane-level digital roadway database, a developed lane-matching algorithm, and a real-time vehicle location display. Lane-level positioning opens up the door for a number of new intelligent transportation system applications such as better fleet management, lane-based traffic measurements from probe vehicles, and lane-level navigation. The developed low-cost system has been tested on a number of roadways and has performed very well when used with accurately surveyed map data. Based on more than 100 000 s, it has correctly determined the lane 97% of the time.

Simulation Study of Bus Signal Priority Strategy

Providing signal priority for buses has been proposed as an inexpensive way to improve transit efficiency and productivity and to reduce operation costs. Bus signal priority has been implemented in several U.S. cities to improve schedule adherence, reduce transit operation costs, and improve customer ride quality. Current signal priority strategies primarily utilize sensors to detect buses at a fixed or preset distance away from an intersection. Traditional presence detection systems, ideally designed for emergency vehicles, usually send a signal priority request after a preprogrammed time offset as soon as transit vehicles are detected without the consideration of bus readiness. The objective of this study is to take advantage of the already equipped Global Positioning System and automated vehicle location system on the buses in Minneapolis, Minnesota, and to develop an adaptive signal priority strategy that could consider bus schedule adherence, number of passengers, location, and speed. Buses can communicate with intersection signal controllers by using wireless technology to request signal priority. Communication with the roadside unit (e.g., traffic controller) for signal priority may be established by using the readily available wireless local area network (WLAN) 802.11x or the dedicated short-range communication (DSRC) 802.11p protocol currently under development for wireless access to and from the vehicular environment. This paper describes the proposed priority logic and its evaluation with the use of microscopic traffic simulation. Simulation results indicate that a 12% to 15% reduction in bus travel time during a.m. peak hours (7 to 9 a.m.) and a 4% to 11% reduction in p.m. peak hours (4 to 6 p.m.) could be achieved by providing signal priority for buses. Average bus delay time was reduced in the range of 16% to 20% and 5% to 14% during a.m. and p.m. peak periods, respectively.

Estimation of Transit Reliability Level-of-Service Based on Automatic Vehicle Location Data

This paper concerns transit quality of service and focuses on reliability. Starting from automated vehicle location (AVL) data collected in a case study, the advantages and limitations of the Transit Capacity and Quality of Service Manual (TCQSM) method for level-of-service (LOS) estimation are discussed. In particular, this method is clear, simple, and easy to use, but sometimes it may lead to inconsistent LOS estimations for three reasons. First, it does not consider the amount of delay but considers only the number of trips that are late. Second, it does not adequately address the effect of early departures on users. Finally, it introduces a fixed tolerance (3 min in advance and 5 min late) around the schedule to consider the trip on time. A new service measure called weighted delay index is proposed; it allows these limitations to be overcome. In this paper, a methodology for its estimation is presented, and new LOS ranges and thresholds for reliability are proposed. Both the TCQSM method and the proposed one have been applied to four routes of the Trieste transit network in Italy. The basic data have been collected through the AVL system during the peak hours of a month. The results have been compared with reference to real data, and it appears that the proposed approach could lead to more graduated LOS estimation because of the higher number of parameters introduced in the new transit service measure.

Designing Automated Vehicle Location Systems for Archived Data Analysis

Automatic vehicle location (AVL) and other automated data collection systems can provide a rich and extensive database that can be analyzed to improve transit management and performance. In the past, many such systems have failed to provide a good data archive, while others have had success. Through the use of an extensive survey and in-depth case studies of nine transit agencies, the key issues in system design that help determine whether a data collection system will provide the useful data archive that many agencies desire are examined. In issues related to design of the data collection system itself, the focus is on five different levels of spatial detail. Issues related to database design are also discussed, as are organizational issues.

Transit Vehicle Arrival Prediction: Algorithm and Large-Scale Implementation

An algorithm is presented to predict transit vehicle arrival times up to 1 h in advance. It uses the time series of data from an automated vehicle location system, consisting of time and location pairs. These data are used with historical statistics in an optimal filtering framework to predict future arrivals. The algorithm is implemented for a large transit fleet in Seattle, Washington, and the prediction results for hundreds of locations are made widely available on the Web. An evaluation of the second busiest but most complex prediction site is presented to demonstrate the value of prediction over the use of schedules alone.