Automatic Vehicle Location Data Research Articles

Quantifying transit travel experiences from the users’ perspective with high-resolution smartphone and vehicle location data: Methodologies, validation, and example analyses

While transit agencies have increasingly adopted systems for collecting data on passengers and vehicles, the ability to derive high-resolution passenger trajectories and directly associate them with transit vehicles in a general and transferable manner remains a challenge. In this paper, a system of integrated methods is presented to reconstruct and track travelers usage of transit at a detailed level by matching location data from smartphones to automatic transit vehicle location (AVL) data and by identifying all out-of-vehicle and in-vehicle portions of the passengers trips. High-resolution travel times and their relationships with the timetable are then derived. Approaches are presented for processing relatively sparse smartphone location data in dense transit networks with many overlapping bus routes, distinguishing waits and transfers from non-travel related activities, and tracking underground travel in a Metro network. The derived information enables a range of analyses and applications, including the development of user-centric performance measures. Results are presented from an implementation and deployment of the system on San Francisco’s Muni network. Based on 103 ground-truth passenger trips, the detection accuracy is found to be approximately 93%. A set of example applications and findings presented in this paper underscore the value of the previously unattainable high-resolution traveler-vehicle coupled movements on a large-scale basis.

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

This paper investigates punctuality at bus stops. Although it is typically evaluated from the point of view of bus operators, it must also account for users, as required in recent service quality norms. Therefore, evaluating punctuality at bus stops is highly important, but may also be a complex task, because data on both bus arrivals (or departures) and users must be taken into account and processed. Data on buses can be collected by Automatic Vehicle Location (AVL) systems, but several challenges must be addressed in order to use them effectively. Passengers data at bus stops cannot be derived from AVL, but they can be used to derive passenger patterns and need to be integrated into processed AVL data. This paper proposes a new punctuality measure defined as the fraction of passengers who will be served within an acceptably short interval after they arrive. A method is proposed to determine this measure: it provides (i) several rules to handle AVL collected data, (ii) a procedure integrating processed AVL data and potential passengers’ patterns and (iii) a hierarchical process to perform the punctuality measure on each bus route direction of a transit network, as well as for every bus stop and time period. The paper illustrates the experimentation of this method on more than 4,000,000 data of a real bus operator and represents outcomes by easy-to-read control dashboards.

“Weather” transit is reliable? Using AVL data to explore tram performance in Melbourne, Australia

This paper uses automatic vehicle location (AVL) records to investigate the effect of weather conditions on the travel time reliability of on-road rail transit, through a case study of the Melbourne streetcar (tram) network. The datasets available were an extensive historical AVL dataset as well as weather observations. The sample size used in the analysis included all trips made over a period of five years (2006–2010 inclusive), during the morning peak (7 am–9 am) for fifteen randomly selected radial tram routes, all traveling to the Melbourne CBD. Ordinary least square (OLS) regression analysis was conducted to create a linear model, with tram travel time being the dependent variable. An alternative formulation of the model is also compared. Travel time was regressed on various weather effects including precipitation, air temperature, sea level pressure and wind speed; as well as indicator variables for weekends, public holidays and route numbers to investigate a correlation between weather condition and the on-time performance of the trams. The results indicate that only precipitation and air temperature are significant in their effect on tram travel time. The model demonstrates that on average, an additional millimeter of precipitation during the peak period adversely affects the average travel time during that period by approximately 8 s, that is, rainfall tends to increase the travel time. The effect of air temperature is less intuitive, with the model indicating that trams adhere more closely to schedule when the temperature is different in absolute terms to the mean operating conditions (taken as 15 °C).

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.

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.

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.

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.

Bus Dwell Time Estimation and Prediction: A Study Case in Shanghai-China

Since dwell time usually takes a large part of bus travel time, the large variability in dwell time always makes accurate prediction of arrival time\ ravel time difficult. On the other hand, Automatic Vehicle Location (AVL) and Automatic Passengers Counters (APC) systems are increasingly implemented for transit operation, which yield a vast amount of real time data. The emphasis of this research is to develop a bus dwell time model based on AVL and APC dynamic data, which is capable of providing real time information on bus arrival times. This model can be used for stop-based control strategies as well. The dwell time model established in this paper not only includes the number of passengers boarding and alighting, but also considers secondary factors like crowding and fare type. The number of boarding and alighting passengers is estimated by passenger arrival rate, bus headway, and capacity. Collection method, service mode, capacity restriction and occupancy of the vehicle are all taken into account in the model. Furthermore, the model is validated with the data of bus line Jiading 3 in Shanghai, China. It is compared with two previously developed models for the same route in four data sets. The results indicate that the models can be well applied in high demanded urban bus lines, especially in presence of high occupancy of vehicles. Since the effectiveness of estimation models is verified by statistical analysis methods, it will help in obtaining a reliable algorithm which can be adopted for bus arrival time/travel time prediction and assessing transit stop-based dynamic control actions.

Potential of Low-Frequency Automated Vehicle Location Data for Monitoring and Control of Bus Performance

The potential of low-frequency bus localization data for the monitoring and control of bus system performance is investigated in this paper. It is shown that data with a sampling rate as low as 1 min, when processed appropriately, can provide ample information. Accurate estimates of stop arrival and departure times are obtained; these estimates in turn allow the analysis of headways and travel times. A three-parameter gamma family of distributions is fitted for headways at the stops along a bus line. The evolution of the parameters demonstrates critical points on the line where bus bunching is significantly increased. Moreover, this analysis allows differentiating problems associated with varying passenger demand from uncertainties associated with traffic conditions. Furthermore it is shown that expected travel time and travel time variability can be calculated from low-frequency localization data. Finally, the way in which the results can be used to calibrate a simulation model that can test bus control strategies is presented. The methods are applied and validated to data obtained from Bus Route Number 1 in Boston, Massachusetts.

Benefits to the Academic Community of Standardization of Bus Transit Automatic Vehicle Location Data

Automatic vehicle location (AVL) systems offer transit agencies and the academic community vast quantities of data on the operation of bus transit networks. However, turning this raw data into useful metrics that are of interest can be cumbersome and requires a significant amount of processing of AV L log files. Both transit agencies and the academic community would benefit if the AV L system output such metrics in a standardized format. This paper summarizes existing data exchange standards used in the bus transit industry and explains their benefits. A recent project undertaken by London Buses to analyze dwell time data is described, and the difficulties and costs involved in collecting the basic data required for analysis are highlighted. The paper proposes that more data standards should be defined for data output by AV L systems. If these standards were then implemented by AV L vendors, transit agencies and academics could get easy access to useful information that otherwise would require much time and cost to obtain.

Measuring Bus Stop Dwell Time and Time Lost Serving Stop with London iBus Automatic Vehicle Location Data

There is considerable research in the literature on the dwell time of buses at bus stops. However, common measures of dwell time do not consider the time lost by the bus decelerating and accelerating from a stationary speed to serve the stop. This paper identifies eight features that are observed when a bus serves a bus stop. These features were used to propose a definition of dwell time that can be measured with bus automatic vehicle location systems. A proposed metric gives the time that would have been saved had the bus stop not been present in the road network. Metrics were calculated for all bus stops on Route 45 in London; more than 50,000 stop visit events were used for the calculation. The time lost arriving at (i.e., decelerating) and departing from (i.e., accelerating) the bus stop was typically 11.6 s. This interval was often much longer than the time lost when the doors were open at the bus stop. Buses were shown to lose significant time serving a bus stop even if the bus never came to a halt at the stop. The method proposed in this paper can be used by transit agencies to measure the actual drive time of buses, removing the component of time lost serving the bus stop. The method can be used to identify bus stops that may need redesign to reduce the time lost in arriving and departing. A novel contribution of this research is that all proposed metrics can be automatically measured with modern automatic vehicle location systems, allowing easy collection of information.

Implementation of an iterative headway-based bus holding strategy with real-time information

In high frequency bus service lines buses often come irregularly at the stops, often in bunches, due to the uncertainty of the passenger demand and behavior and unexpected conditions on the roads. Vehicle holding is a commonly used strategy among a variety of control strategies in transit operation in order to reduce bus bunching and regulate bus headways. This paper investigates a control strategy of holding a group of buses at a single or multiple control point(s). By incorporating any passenger boarding activities during holding, a single control point problem is developed and extended to multiple control points to reduce the variance of headways for the downstream stops. The problem is a non-convex optimization problem with linear constraints that minimizes the total passenger waiting time both on-board and at stops. A heuristic is then developed that is easy and fast to implement, which makes it suitable for real-time implementation. The model is evaluated with a simulation case study by using the real-time bus operation data (i.e., Automatic Vehicle Location and Automatic Passenger Count Data) from the Chicago Transit Authority (CTA). The simulation results show that considering the boarding activities in the total waiting time, our model mitigates the error propagation and maintains steady performance, compared to the common models in the literature, which do not consider boarding while holding.

Spatial and temporal visualization of transit operations performance data at a network level

The operational performance of public transport networks is an important aspect of urban planning and development. Despite the network focus of services and delays which are often focused spatially, little research has examined the spatial pattern of operational performance of urban public transport.This paper explores the operational performance of on-road public transport using spatial and historical analysis at a network wide level. Operational performance is investigated through analysis of stop level Automatic Vehicle Location (AVL) data and utilizes visualization techniques in geographical information systems (GISs) to explore spatial and temporal patterns of change. The network in question is the Melbourne tram (streetcar) network, which has a large historical dataset of AVL data since 2001 providing information on scheduled arrival times, actual arrival times, times of departure, and more.Statistical analysis is undertaken to determine average travel times and the standard deviation of travel times. The statistical analysis is then imported into a GIS package for further analysis; the Inverse Distance Weighted (IDW) methodology is used to explore spatial variations in data. Outputs include observation of travel times across the network, a reliability analysis investigating actual vs. scheduled travel times and coefficients of variation, and trend analyses to observe the change in tram travel time and change in coefficient of variation.Through this analysis, various changes in travel time and reliability were observed between 2001 and 2010. Vast areas saw improvements in travel time and reliability, while some areas were identified to have deteriorated performance. This paper identifies that the spatial representation of large datasets is of great value in an ongoing monitoring of the network and a powerful reporting tool on a network level basis. Moreover, the results identify transit issues for the Melbourne tram network planners for further investigation.

Data management and applications in a world-leading bus fleet

Automatic Vehicle Location (AVL) Systems are being introduced increasingly in many major cities around the world to improve the efficiency of our road-based passenger transport systems. Satellite-based location and communication systems, particularly the Global Positioning System (GPS) have been the platform for AVL systems which are now supporting real-time passenger information (RTPI), fleet management and operations (FMOs) and public transport priorities (PTPs), to name three key applications. The process of real-time on-board bus location can result in a substantial database where the progress of the bus is stored typically on a second-by-second basis. This is necessary for the primary real-time applications such as those listed above (e.g. RTPI, FMO and PTP). In addition, it is clear that such data could have an array of ‘secondary’ purposes, including use off-line for improving scheduling efficiency and for automatic performance monitoring, thus reducing or removing the need for manual on-street surveys. This paper looks at these and other innovative uses of AVL data for public transport, taking the recent iBus system in London as a current example of a modern AVL/GPS application in a capital city. It describes the data architecture and management in iBus and then illustrates two further examples of secondary data use – dwell time estimation and bus performance analysis. The paper concludes with a discussion of some key data management issues, including data quantity and quality, before drawing conclusions.

Quantifying Reliability of Transit Service in Zurich, Switzerland

Transit operators aim to provide high levels of reliability because reliable services not only are more attractive for their current and potential customers, but can also reduce operating costs by making more efficient use of resources. This work addresses the issue of service reliability in a context in which high-quality transit is the norm, not the exception. An ongoing study uses automatic vehicle location (AVL) data to estimate quantitative service reliability measures of a bus line in Zurich, Switzerland. In-vehicle observations during peak periods and aggregate operational data reports complement the AVL data. Four time profiles (morning and late afternoon peaks, between peaks, and late evening) were used to characterize transit service reliability at the route and stop level. A set of conventional operator-perspective measures (travel time, speed, punctuality, and regularity) was enhanced with a customer-oriented measure (passenger waiting time) to link service reliability to passenger waiting cost. Results indicate that the best service reliability for travel time, regularity, and passenger waiting time metrics is achieved during late night services. Services between peak hours perform better in punctuality metrics.

Bus-Holding Control Strategies

Transit operations involve several inherent sources of uncertainty, including dispatch time from the origin terminal, travel time between stops, and dwell time at stops. Bus-holding control strategies are a prominent method applied by transit operators to improve transit performance and level of service. The common practice is to regulate departures from a limited number of stops by holding buses until their scheduled departure time. An analysis of the performance of a high-frequency bus line in Stockholm, Sweden, based on automatic vehicle location data showed that this control strategy was not effective in improving service regularity along the line. The analysis also indicated that drivers adjusted their speed according to performance objectives. Implications of a control strategy that regulates departures from all stops on the basis of the headways of the preceding bus and the following bus were evaluated with BusMezzo, a transit operations simulation model. The results suggest that this strategy can improve service performance considerably from both passengers' and operator's perspectives. In addition, the strategy implies cooperative operations, as the decisions of each driver are interdependent with other drivers' decisions, and mutual corrections can be made. Difficulties in realizing the benefits of the proposed strategy in practice, such as dispatching from the origin terminal, driver scheduling, and compliance, are discussed. The implications of several practical considerations are assessed by conducting a sensitivity analysis as part of the preparations for a field experiment designed to test the proposed control strategy.

Evaluate bus emissions generated near far-side and near-side stops and potential reductions by ITS: An empirical study

In this paper, we use second-by-second automatic vehicle location data to estimate bus emissions near far-side and near-side stops. We classify the bus running state near a stop into approach, dwell, and departure. A vehicle specific power approach is used to estimate bus emissions for each state. We show that bus emissions generated near stops can be significantly reduced by using certain intelligent transportation systems techniques.

Bus-Arrival-Time Prediction Models: Link-Based and Section-Based

Bus-arrival-time information service is a key component of an advanced transit information system. Instantaneous and accurate prediction of bus arrival time can help improve the quality of service, and attracts additional ridership. On the bases of analyses of bus running processes, bus arrival time was divided into interzone link travel time and section travel time. A self-adaptive exponential smoothing-based algorithm was proposed for interzone link travel time prediction, whereas link-based and section-based algorithms were proposed for section travel time prediction. With the automatic vehicle location data collected from an actual bus route, an experiment was conducted to measure the performance (accuracy and precision in prediction) of link-based and section-based models with respect to three dominant factors: day of week, time of day, and length of segment. The research results show that: (1) the overall performance of section-based models is superior to that of link-based models on all weekdays bu...