Automatic Vehicle Location Research Articles

AVL and Monitoring for Massive Traffic Control System over DDS

This paper proposes a real-time Automatic Vehicle Location (AVL) and monitoring system for traffic control of pilgrims coming towards the city of Makkah in Saudi Arabia based on Data Distribution Service (DDS) specified by the Object Management Group (OMG). DDS based middleware employs Real-Time Publish/Subscribe (RTPS) protocol that implements many-to-many communication paradigm suitable in massive traffic control applications. Using this middleware approach, we are able to locate and track huge number of mobile vehicles and identify all passengers in real-time who are coming to perform annual Hajj. For validation of our proposed framework, various performance matrices are examined over WLAN using DDS. Results show that DDS based middleware can meet real-time requirements in large-scale AVL environment.

Modeling Bus Travel Time Reliability with Supply and Demand Data from Automatic Vehicle Location and Smart Card Systems

Travel time reliability is an important aspect of bus service quality. Despite a significant body of research on private vehicle reliability, little attention has been paid to bus travel time reliability at the stop-to-stop link level on different types of roads. This study aims to identify and quantify the underlying determinants of bus travel time reliability on links of different road types with the use of supply and demand data from automatic vehicle location and smart card systems collected in Brisbane, Australia. Three general bus-related models were developed with respect to the main concerns of travelers and planners: average travel time, buffer time, and coefficient of variation of travel time. Five groups of alternative models were developed to account for variations caused by different road types, including arterial road, motorway, busway, and central business district. Seemingly unrelated regression equations estimation were applied to account for cross-equation correlations across regression models in each group. Three main categories of unreliability contributory factors were identified and tested in this study, namely, planning, operational, and environmental. Model results provided insights into these factors that affect bus travel time and its variability. The most important predictors were found to be the recurrent congestion index, traffic signals, and passenger demand at stops. Results could be used to target specific strategies aimed at reducing unreliability on different types of roads.

The Farside Story

Determining the proper location of bus stops is an important planning decision in the transit planning field. While previous efforts in the literature have suggested several advantages and disadvantages of certain bus stop placements, there has been little effort toward understanding the impacts of bus stop location on the transit system performance at the stop level of analysis. This paper evaluates the impact of bus stop location on bus stop time and stop time variation. The paper uses stop level data collected from the Société de Transport de Montréal's automatic vehicle location and automatic passenger counting systems in Montreal, Quebec, Canada. The study findings show that stop times occurring on the nearside of intersections are on average 4.2 to 5.0 s slower than stop times occurring on the farside of intersections, with no impact on stop time variation. A validation model was used to confirm the impacts of bus stop placements on stop time with data from TriMet's automated bus dispatch system in Portland, Oregon. This study offers transit planners and policy makers a better understanding of the effects of bus stop location on stop time and its variation to improve service quality while minimizing service variation at the stop level.

Development of Application for Estimating Daily Boarding and Alighting Counts on New York City Buses

To support bus service scheduling and planning, New York City Transit (NYCT) put into production a ridership application to determine surface transit boarding and alighting locations for each of approximately 2.8 million daily passenger trips on 218 bus routes. The application combined data from an automated vehicle location (AVL) system, a multimodal entry-only nongeographic automated fare collection (AFC) system, and general transit feed specification schedule file streams. To accomplish this objective, NYCT developed a highly optimized network-generation tool to estimate bus link loads and boarding and alighting locations by creating a scaled-down custom network based on first-bus trajectory (from AVL boarding location data) and a few possible AFC- or AVL-inferred second-leg pickup stops. Solving for the shortest walking path on this subnetwork yielded connection and alighting points more efficiently than did solving for all 128 million potential origin–destination (O-D) pairs systemwide. The program executes in less than 3 h in an automated environment that can support the operations management need for next-day reporting and the monitoring of patterns and trends in ridership behavior. The program also allows the aggregation of multiple days of route-level program output for schedule-making purposes and thus provides a significantly more representative understanding of passenger loads than were obtained historically from a few labor-intensive onboard observations (ridechecks) collected over a multiple-year period. Results were validated and found to be consistent with manual ridechecks, limited O-D surveys, and other sources. The accuracy obtained was sufficient to achieve acceptance of AVL-AFC data in lieu of traditional onboard observations for NYCT schedule making.

Patch-based Modelling of City-centre Bus Movement with Phase-type Distributions

We propose a methodology for constructing a stochastic performance model of a public transportation network using real-world data. Our main data source consists of Automatic Vehicle Location (AVL) measurements of buses in the Edinburgh region. Although the data has a relatively low frequency, we can use it to parameterise a model in which a bus moves between predefined patches in the city. We fit the probability distributions of the sojourn times in the patches to phase-type distributions using the tool HyperStar. We then translate the output from HyperStar to a model of a complete part of a bus route expressed in the reactive modules language of the PRISM model checker. Finally, we demonstrate how we can use the numerical techniques implemented in PRISM to answer meaningful questions about the performance of the bus network in the context of a case study involving the addition of trams to a busy section of Edinburgh's city centre.

Transit Travel Time Reliability

Transit agencies traditionally have used vehicle movement to assess service quality. However, new technologies such as automatic fare collection systems, automatic passenger counters, and automatic vehicle location devices are giving agencies a wealth of data from which critical insights can be gained about customer experience. Trip-level travel time distributions are used to explore the potential for a new customer-focused measure of service quality. The proposed method builds on recent transit research and ties in to lessons learned over the past 20 years of highway reliability research. An example of delay incidents illustrates the disconnect between the vehicle-focused tools that currently are available to transit agencies for the evaluation of service quality (e.g., minutes of train delay, headway adherence) and what a customer cares about (i.e., travel time to the destination). This disconnect is addressed with the use of actual trip times to outline a new measure: percentage of customers with a travel time of less than x min. Historical travel time distributions create the opportunity to understand better the duration and magnitude of an incident. Possible applications of information about travel time reliability are identified; these include improved understanding of the causes of delay, incident management, and communication of service status to customers.

Exploring Applications of Second-Generation Archived Transit Data for Estimating Performance Measures and Arterial Travel Speeds

Travel time and operating speed influence the attractiveness, operating cost, and system efficiency of transit service. As part of its bus dispatch system, the Tri-County Metropolitan Transportation District of Oregon has been archiving automatic vehicle location (AVL) and automatic passenger count data for all bus trips at the stop level since 1997. In 2013, a new and higher-resolution bus AVL data collection system was implemented. This new system provides stop-level data as well as 5-s resolution (5-SR) bus position data between stops. The objective of this paper is to explore potential applications of the new data for assessing transit performance and for estimating transportation system performance measures for urban arterials. Results suggest that the 5-SR data provide high-resolution information on time and position that can be used to determine bus travel speeds between bus stops, identify speed breakdowns, and estimate intersection signal and queuing delays.

Bus Travel Time Deviation Analysis Using Automatic Vehicle Location Data and Structural Equation Modeling

To investigate the influences of causes of unreliability and bus schedule recovery phenomenon on microscopic segment-level travel time variance, this study adopts Structural Equation Modeling (SEM) to specify, estimate, and measure the theoretical proposed models. The SEM model establishes and verifies hypotheses for interrelationships among travel time deviations, departure delays, segment lengths, dwell times, and number of traffic signals and access connections. The finally accepted model demonstrates excellent fitness. Most of the hypotheses are supported by the sample dataset from bus Automatic Vehicle Location system. The SEM model confirms the bus schedule recovery phenomenon. The departure delays at bus terminals and upstream travel time deviations indeed have negative impacts on travel time fluctuation of buses en route. Meanwhile, the segment length directly and negatively impacts travel time variability and inversely positively contributes to the schedule recovery process; this exogenous variable also indirectly and positively influences travel times through the existence of signalized intersections and access connections. This study offers a rational approach to analyzing travel time deviation feature. The SEM model structure and estimation results facilitate the understanding of bus service performance characteristics and provide several implications for bus service planning, management, and operation.

Methods for pre-processing smartcard data to improve data quality

In recent years smartcards have been implemented in many transit systems around the world as a means by which passengers pay for travel. In addition to allowing speedier boardings there are many secondary benefits of smartcard systems including better understanding of travel patterns and behaviour of travellers. Such research is dependent on the smartcard correctly recording the boarding stop, and where available the alighting stop. It is also dependent on the smartcard system correctly aggregating individual rides into trips.This paper identifies causes for why smartcard systems may not correctly record such information. The first contribution of the paper is to propose a set of rules to aggregate individual rides into a single trip. This is critical in the research of activity based modelling as well as for correctly charging the passenger. The second contribution of the paper is to provide an approach to identify erroneous tap-out data, either caused by system problems or by the user. An approach to detecting this phenomenon is provided. The output from this analysis is then used to identify faulty vehicles or data supply using the “comparison against peers approach”. This third contribution of the paper identifies where transit agencies and operators should target resources to improve performance of their Automatic Vehicle Location systems. This method could also be used to identify users who appear to be tapping out too early.The approaches are tested using smartcard data from the Singapore public transport network from one week in April 2011. The results suggest that approximately 7.7% of all smartcard rides recorded the passenger as alighting one stop before the bus stop that they most probably alighted at. A further 0.7% of smartcard rides recorded the passenger as alighting more than one stop before the bus stop that they most probably alighted at. There was no evidence that smartcards overestimated the distance travelled by the passenger.

Validating the coverage of bus schedules: A Machine Learning approach

Nowadays, every public transportation company uses Automatic Vehicle Location (AVL) systems to track the services provided by each vehicle. Such information can be used to improve operational planning. This paper describes an AVL-based evaluation framework to test whether the actual Schedule Plan fits, in terms of days covered by each schedule, the network’s operational conditions.Firstly, clustering is employed to group days with similar profiles in terms of travel times (this is done for each different route). Secondly, consensus clustering is used to obtain a unique set of clusters for all routes. Finally, a set of rules about the groups content is drawn based on appropriate decision variables. Each group will correspond to a different schedule and the rules identify the days covered by each schedule.This methodology is simultaneously an evaluator of the schedules that are offered by the company (regarding its coverage) and an advisor on possible changes to such offer. It was tested by using data collected for one year in a company running in Porto, Portugal. The results are sound.The main contribution of this paper is that it proposes a way to combine Machine Learning techniques to add a novel dimension to the Schedule Plan evaluation methods: the day coverage. Such approach meets no parallel in the current literature.

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.

Estimation of bus dwell time using univariate time series models

SummaryA significant proportion of bus travel time is contributed by dwell time for passenger boarding and alighting. More accurate estimation of bus dwell time (BDT) can enhance efficiency and reliability of public transportation system. Regression and probabilistic models are commonly used in literatures where a set of independent variables are used to define the statistical relationship between BDT and its contributing factors. However, due to technical and monetary constraints, it is not always feasible to collect all the data required for the models to work. More importantly, the contributing factors may vary from one bus route to another. Time series based methods can be of great interest as they require only historical time series data, which can be collected using a facility known as automatic vehicle location (AVL) system. This paper assesses four different time series based methods namely random walk, exponential smoothing, moving average (MA), and autoregressive integrated moving average to model and estimate BDT based on AVL data collected from Auckland. The performances of the proposed methods are ranked based on three important factors namely prediction accuracy, simplicity, and robustness. The models showed promising results and performed differently for central business district (CBD) and non‐CBD bus stops. For CBD bus stops, MA model performed the best, whereas for non‐CBD bus stops, ARIMA model performed the best compared with other time series based models. Copyright © 2014 John Wiley & Sons, Ltd.

Measuring Service Reliability Using Automatic Vehicle Location Data

Bus service reliability has become a major concern for both operators and passengers. Buffer time measures are believed to be appropriate to approximate passengers' experienced reliability in the context of departure planning. Two issues with regard to buffer time estimation are addressed, namely, performance disaggregation and capturing passengers’ perspectives on reliability. A Gaussian mixture models based method is applied to disaggregate the performance data. Based on the mixture models distribution, a reliability buffer time (RBT) measure is proposed from passengers’ perspective. A set of expected reliability buffer time measures is developed for operators by using different spatial-temporal levels combinations of RBTs. The average and the latest trip duration measures are proposed for passengers that can be used to choose a service mode and determine the departure time. Using empirical data from the automatic vehicle location system in Brisbane, Australia, the existence of mixture service states is verified and the advantage of mixture distribution model in fitting travel time profile is demonstrated. Numerical experiments validate that the proposed reliability measure is capable of quantifying service reliability consistently, while the conventional ones may provide inconsistent results. Potential applications for operators and passengers are also illustrated, including reliability improvement and trip planning.

Automatic Vehicle Location and Monitoring System based on Data Distribution Service

This paper proposes a real time Automatic Vehicle Location (AVL) and Monitoring system for pilgrims road transport coming towards city of Makkah in Saudi Arabia based on Data Distribution Service (DDS). This service is a real time publish/subscribe middleware. Using this middleware approach, we are able to locate and track a huge number of mobile vehicles and identify pilgrims for an annual Islamic gathering in the Holy City of Makkah. Performance results are demonstrated for LAN, WLAN and Bluetooth over DDS.

Travel Time of Public Transport Vehicles Estimation

Effective prediction of speed is central to advanced traveler information and transportation management systems. The speed of public transport vehicles is affected by many external factors including traffic volume, organization and infrastructure. The literature presents methods for estimating travel time on sections of a transport network and vehicle arrival at stops, often making use of the AVL (automatic vehicle location). The aim of the authors of this paper is to identify these factors, their impact and significance on the average speed of public transport vehicles in selected sections of the transport network. The paper presents the results of field studies involving the measurement of travel time of public transport vehicles in the main streets of the Tri-City Agglomeration (Gdansk, Sopot, Gdynia). The study was carried out within the framework of the project for the construction of the urban traffic control system TRISTAR. Based on the collected data the authors built a model of the relationship between the average speed of public transport vehicles and these external factors. The results will be used to calibrate the macroscopic transport model in the Tri-City Agglomeration.

Transitory Optimism

Passengers’ perception and satisfaction have long been seen and used as important measures of transit service quality and attractiveness. This research tried to understand better transit passengers’ perception of the implementation of various improvement strategies in bus service over time. The study analyzed three surveys of bus user perceptions conducted over a period of 3 years. The study also used stop-level data collected from the Société de Transport de Montréal's automated vehicle location and automatic passenger count systems and bus schedules in Montreal, Canada, to measure the actual changes in service. Descriptive statistics and regression models were used for a better understanding of the differences between perceptions and reality. The implementation of various strategies had a limited impact on the short-term overestimation by users of their waiting time benefits, whereas the implementation had a long-term impact on their travel time overestimation. This study can be of interest to marketing and planning departments at transit agencies, because it provides them with new insights into passengers’ perception and satisfaction.

A Review on Vehicular Monitoring and Tracking

In today's world the life has become very fast. The factors like increasing pollution and increasing population has affected day-to-day life. The number of vehicles and undisciplined traffic has invited the number of accidents every day. Road accidents are ever increasing in our country also. The victims are losing their lives as there is inordinate delay in reaching the hospitals and getting the medical treatment well within the time. Also, with rapid urbanization, vehicles are also increasing. More the number of vehicles more are probability of accidents. The government took a number of initiatives and many awareness programs but accident rate remains high. Keeping in view the ever increasing death cases on the road accidents, there are different methods to track the vehicle at the soonest possible time and rescue the accidents. In this paper we compare three different vehicle tracking techniques. First is by using arm processor second is by using AVL (Automatic Vehicle Location system) and third is by using on-line fusion of color and shape features.

Bus Drivers’ Responses to Real-Time Schedule Adherence and the Effects on Transit Reliability

Bus drivers have responded positively toward real-time bus information in various surveys. However, empirical studies on their actual responses are limited. On the basis of actual automatic vehicle location data, this study quantified bus drivers’ responses to real-time schedule adherence and their effects on transit reliability. Bus trips that were ahead of and behind schedule were analyzed separately at timepoint stops, regular stops, and along the roadways between stops. Results revealed that bus drivers would use real-time information to keep on schedule. Early buses were found to be more likely to make adjustments in response to information than were late buses along the roadways. Moreover, bus drivers’ responses to real-time information was found to improve transit reliability: 50% of the improvement was the result of drivers’ responses to schedule adherence at timepoint stops and 50% was the result mainly of drivers’ responses to schedule adherence along the roadways. The likelihood that drivers would make adjustments at regular stops to adhere to schedule was low.

Highway versus Urban Roads

In this study, the differences in travel time variability patterns between urban roads and highways were analyzed. For urban roads, a GPS data set that included all taxi trips in New York City was used. For highways, automatic vehicle location data from the New Jersey Turnpike were used. The turnpike was divided into two sections, urban and suburban highway, according to urbanization level, time-of-day demands, and physical roadway features. The analysis not only compared travel time patterns for highways and urban roads but also investigated travel time characteristics along the same highway facility. First, the temporal variations of travel times on both facility types were calculated and compared. Second, the travel time distributions were extracted for various time periods and compared visually to determine the distributional patterns. Finally, the relationship between the average travel time and variability was investigated. Not only did travel time patterns differ between urban roads and highways, but major differences in travel time characteristics could be observed along the same highway. Higher travel times corresponded to lower reliability on highways yet corresponded to higher reliability on urban roads. Overall, the findings suggest that attributing differences in travel time variability patterns to facility type may be an oversimplification of the phenomenon.

All aboard at all Doors

The time that buses spend waiting for passengers to board can be a significant portion of a bus route's overall running time. A key determinant of boarding time is the number of doors through which passengers are permitted to board. Transit agencies that allow boarding through all doors, instead of just through the front door, typically enjoy decreased boarding times and decreased running times. This study focused on the feasibility of an all-door boarding policy for La Société de transport de Montréal (STM), the public transit agency of Montreal, Canada. The potential benefits of such a policy were assessed through three main steps. First, a selection methodology was developed to determine which of STM's bus routes would benefit most from various all-door boarding strategies. Second, a multivariate regression analysis was implemented with STM's archived automatic vehicle location and automatic passenger counter data to estimate the dwell and running-time savings that would result under various implementation scenarios. Third, a sensitivity analysis was developed to demonstrate the savings associated with implementing the policy. The findings showed that all-door boardings could yield substantial savings in running time, with morning peak savings as much as 15.8% on the best routes. In many cases, the running-time savings were enough to remove a bus from a route while still maintaining existing frequencies. The findings from this research may be beneficial for transit planners and operators since the presented methodologies show substantial savings from all-door boarding and can be adopted by other transit agencies.