Simulating a Regularly Sampled: Bus Location System

Vehicle arrival time is one of the most important factors of intelligent transportation systems (ITS). Accurate transit travel information is important because it attracts additional customers and increases the satisfaction of transit users. A passenger waiting for a train or bus, a person waiting for a cab, a customer waiting for a courier to come to his/her home to pickup or deliver a package, a business office waiting for a truck for goods and a home user waiting for his/her shipment for which he/she did online shopping are a few examples of how important vehicle arrival time is in different areas of life. Most companies are investing a lot of money to improve their systems for better, faster and reliable customer service. As the cost of ITS components have decreased, the automatic vehicle location (AVL) system, which is one component of ITS, has become more widely used. Many transit agencies use an AVL system to track their vehicles in real-time. Tracking systems technology was made possible by the integration of three technologies: global positioning system (GPS), global system for mobile communication (GSM) and the geographic information system (GIS). This project shows detailed research in the area of automatic vehicle location and implements a low cost vehicle tracking system using GPS and GPRS. The system reads the current position, speed and direction using GPS, the data is sent via GPRS service from a GSM network to a server using TCP/IP protocol and the server saves this information to the database on a regular time interval. The web-based application then uses this data and calculates the approximate arrival time. The system allows a user to view the present position of the vehicle using Google Maps and calculates the arrival time. Also, bus location can be monitored in real time by route supervisors. This will allow supervisors to make better service adjustment decisions because they will be able to see how the route is operating. The test bed was a bus route running in the downtown of Toronto.

Automatic Vehicle Location and Computer-Aided Dispatch Systems: Design and Application Considerations

An effective solution to traffic jam is to develop public traffic. According to global positioning system (GPS) vehicle positioning system the authors get the bus operation data. Then on the basis of the analysis of the current research, the authors build a more accurate calculation model with extreme value theory, which is used to evaluate the bus running between adjacent sites based on the automatic vehicle location (AVL) data and data mining. The effectiveness of the model is evaluated by a computational example using the AVL data of Shanghai’s No.49 bus. At last, the authors analyze the important role of this index for users and managers, and give some suggestions.

Advanced traveler information systems (ATIS) are one component of intelligent transportation systems (ITS), and a major component of ATIS is travel time information. Automatic vehicle location (AVL) systems, which are a part of ITS, have been adopted by many transit agencies to track their vehicles and to predict travel time in real time. Because of the complexity involved, there is no universally adopted approach for this latter application, and research is needed in this area. The objectives of the research in this paper are to develop a model to predict bus arrival time using AVL data and apply the model for real-time applications. The test bed was a bus route located in Houston, Texas, and the travel time prediction model considered schedule adherence, traffic congestion, and dwell times. A historical data-based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed both the historical data-based model and the regression model in terms of prediction accuracy. It was also found that the ANN models can be used for real-time applications.

New technologies such as automatic vehicle location (AVL) and automatic passenger counters (APC) make tremendous amounts of data available to transit planners and operators. Transit agencies would like to use these data to inform service planning and management and ultimately to provide more reliable service. This requires data processing in such a way as to provide pertinent information to transit planners. The research presented considers a sample of data collected from Chicago Transit Authority buses during the initial stage of AVL and APC implementation in Chicago, Illinois. Methods were developed for extracting information from these data that could be used to compute service reliability indicators. This research also discusses some of the challenges encountered in the data collection process. At the time of the data collection, the home garage for the bus route under consideration was not fully stocked with AVL-equipped buses. Other challenges included the misplacement of some time points and undercounting by the APC system. The data proved useful even with these challenges, and valuable information, such as bus travel patterns, schedule adherence, and headway regularity, was gained from this study. By recognizing some of the challenges faced in the data collection process, this research provided insights that can be used to inform future larger-scale studies of transit operations using AVL and APC data.

This paper presents a study of bus services operational performance through the analysis at a stop level of Automatic Vehicle Location (AVL) data, using the capability of Geographic Information Systems (GIS) to analyze spatial and temporal patterns of change. In particular, the analysis is focused on the study of the reliability of public transport services that connect the most important urban centers and the airport of a low density region. The intention is to suggest transport demand management policies aimed at shifting the modal split towards public transport modes as sustainable forms of mobility, encouraging intermodality. In order to determine average travel times and the standard deviation of travel times a specific statistical analysis has been carried out. The results of this analysis have been imported into a GIS package for a further investigation of the services reliability to highlight some critical aspects and adopt specific policies and opportune solutions.

Recent advances in wireless communications and networks have integrated relatively new technologies such as Global Positioning System (GPS), to the popular Global System for Mobile Communication (GSM), second generation cellular systems and the Geographic Information Systems (GIS) technologies. Automatic Vehicle Location (AVL) is based on a combination of GPS, GIS and telecommunication technologies. Automatic Vehicle Tracking systems are more and more used for different purposes, especially those related to tracking one vehicle or a fleet of vehicles. In this work, we introduce a new AVL system, which is based and developed under GIS software environment. The centralized software at the control station offers a new technology of transferring the intelligence of tracking system from the car unit, into the control office PC software. Centralized software will reduce the programming efforts in the car unit and will offer better fleet management. Moreover, the core of our system is based on the objects or the controllers of the GIS software, which reduces dramatically the overall system cost. Our system provides an easy access to change the functions of the system, with great possibility to satisfy the local needs. The design of our software will be presented with an explanation of the new supporting technologies that were to create the system. Finally, our software system has been validated using data from local road networks. Copyright © 2005 John Wiley & Sons, Ltd.

Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns

Many intelligent transportation system services are making use of vehicle position and time information to help manage traffic congestion and in the case of public transportation, improve service reliability. Position and time data can be acquired in a variety of ways including probe vehicles, embedded road sensors, tagging electronic toll collection transponders, automatic vehicle location (AVL) systems, etc. As part of advanced public transportation systems, many transit agencies are placing AVL systems on their buses to better manage their fleet and to provide their passengers with better information on vehicle arrivals and departures. Another public transport system that can benefit from this technology is shared-use vehicle systems (i.e., car-sharing, station cars). In particular, multiple-station shared-use vehicle systems need to manage their fleet closely to avoid vehicle distribution problems at their stations. In this paper, we describe a route prediction and time-of-arrival estimation methodology that has been implemented with a real-world multiple-station car-sharing system. Each vehicle has A VL hardware, producing position and time data that can be used at the system level for estimation. This is slightly different than bus arrival estimation techniques in that the routes are not known a priori. The developed algorithm was tested against actual routes and arrival times, producing encouraging results. It was determined that the estimation procedure could be improved if the frequency of position/time data was increased.

Recent developments in technologies such as Automatic Vehicle Location (AVL) and advanced control systems have stimulated new interest in bus priority facilities using traffic signals. The use of AVL systems has generated opportunities for implementing flexible busspecific priority strategies according to performance. The extent of the opportunities available depends very much on the architecture of a bus priority system, including the location(s) of intelligence determining the priority level and its implementation, and the method of priority request to the traffic signal. These aspects are important from the point of view of bus priority performance, communication requirements and the cost of the system. This paper draws together and compares the various architectures currently being used for AVL-based bus priority, providing an overall review to supplement other papers which are usually concerned with individual systems. The paper reviews current bus priority systems used across Europe under five different architecture categories. The present bus priority architecture in London is then analysed in more detail as a case study. The paper concludes with a discussion of issues for this application, given the continuing advances in locations and communication technologies, and issues for the future.

If transit agencies wish to retain and attract riders, they need to provide reliable and efficient services. Transit agencies tend to run high-frequency bus routes during peak hours, and in many cities, different routes can also overlap along major corridors. In some instances, consecutive buses can arrive at a shared stop simultaneously or one bus may arrive while another bus is currently servicing the stop. This phenomenon, known as bus bunching, can delay buses and passengers, and is usually inefficient. In this study, we attempt to understand how bus bunching from the same or different routes can impact bus operations, specifically dwell and running times. This research uses stop-level records obtained from automatic vehicle location (AVL) and automatic passenger counter (APC) systems from TriMet, Portland, OR. Using linear modeling, we find that bus bunching increases both dwell and running times. Specifically, when different routes bunch or are scheduled to arrive at a bus stop within a short time frame, or when buses from the same route arrive with a short time frame, dwell times increase by ~10 s. Similarly, bus bunching from the same route or different route prolongs running times by ~40 s. Our findings suggest that bus schedulers and operators should consider adding more time between consecutive buses from different routes at shared stops to minimize the negative impacts that we observed from bus bunching.

Bus priority is being widely implemented across many UTC areas in London, following successful field trials in the EC-funded projects PROMPT and INCOME. Installation has now been completed at over 100 junctions, with full evaluations being undertaken in each case. The remaining roll-out programme includes over 500 additional junctions. This roll-out has initially been based on the existing technology of bus transponders and inductive loop technology. However, the committed London-wide implementation of automatic vehicle location (AVL) technology for fleet management and passenger information at bus stops offers the potential to use new technology and more sophisticated control for bus priority. In particular, the real-time bus location information, provided by AVL, enables the level of priority provided for each bus to be tailored to its needs. The possibility of using AVL to support bus priority at traffic signals in London was raised in the early 1990s, when the first AVL-based COUNTDOWN trials were commissioned. A feasibility study undertaken for London Transport Buses (LTB) by the Transportation Research Group (TRG) at Southampton University indicated potential benefits and likely cost-effectiveness. A method of selective priority for buses, that differentiates between buses based on their headways, was developed and evaluated by TRG using simulation. This method was integrated into the bus priority system, and known as the Headway Regularity Algorithm. System commissioning on a section of the Uxbridge Road was undertaken in 1998/99, leading to technical trials and the first live trials in June/July 1999. The paper summarises this research, development and installation process before describing the first results of the trials in more detail<br/><br/>

An Automatic Vehicle Location (AVL) system utilizing LORAN-C which has been cost-optimized is described. The various sources of positioning errors are enumerated and their effects are quantified through theoretical and empirical techniques. These results are used to generate a performance specification for the continental U.S. This specification is compared to field test results from two operating demonstration systems.

Public transport vehicles often share a road network with other road users making their journeys susceptive to changing road conditions and especially to congestion. Travelers using such public transport increasingly depend on real-time information to plan their journeys. While such information can be provided by Automatic Vehicle Location (AVL) systems, AVLs depend heavily on large-scale deployment of designated sensory equipment, which may prevent their pervasive adoption. This paper presents a system for estimating vehicle location based on information generated by data sources typically integrated within existing ITS platforms. This enables location estimation for public transport vehicles without the need for deploying a designated sensor infrastructure in each vehicle, thereby reducing deployment and maintenance cost significantly. A prototypical vehicle location estimation system has been realized as part of and using data provided by the iTransIT ITS framework. Initial evaluation results show that such a system is feasible in a distributed manner and that estimated results are within 20% compared to empirical data.

A dispatcher can improve a fleet's productivity by knowing where each vehicle in the fleet is located. This productivity gain is independent of whether reduced fleet costs, increased tonnage capacity, safe delivery of secure cargo or less down time due to distracted drivers is management's goal. This paper describes the portion of an automatic vehicle location system which has been designed to enhance fleet management depending on a variety of efficiency concerns. This subsystem, referred to as the Fixed End, is where the dispatcher manipulates the collected location information to make the decisions required to improve the fleet's performance. The Fixed End subsystem has three fundamental tasks it must perform: it must accept commands from the dispatcher then display and update the requested information, it must allow the dispatcher to control the communication rate between the Fixed End and the mobiles and it must allow the dispatcher to customize certain internal data structures to the desired use.