Automatic Vehicle Location Data Research Articles

A General Dynamic Sequential Learning Framework to Vehicle Trajectory Reconstruction Using Automatic Vehicle Location or Identification Data

A General Dynamic Sequential Learning Framework to Vehicle Trajectory Reconstruction Using Automatic Vehicle Location or Identification Data

Statistical modelling of bus travel time with Burr distribution

A better understanding of the travel time distribution shape or pattern could improve the decision made by the transport operator to estimate the travel time required for the vehicle to travel from one place to another. Finding the most appropriate distribution to represent the day-to-day travel time variation of an individual link of a bus route is the main purpose of this study. Klang Valley, Malaysia is the study area for the research. A consecutive of 7 months ten bus routes automatic vehicle location (AVL) data are used to examine the distribution performance. The leading distribution proposed for the research is the Burr distribution. Both symmetrical and asymmetrical distributions that have been proposed in existing studies are also used for comparison purposes. Maximum likelihood estimation is applied for parameter estimation while loglikelihood value, Akaike information criterion (AIC) and Bayesian information criterion (BIC) are applied for performance assessment of the distributions. Promising results are obtained by the leading model in all different kinds of operating environment and could be treated as the preliminary preparation for further reliability analysis.

Determining an efficient and precise choice set for public transport based on tracking data

To understand the route choices of public transport users, it is important to know the information available to them, and the context present at that moment. In fact, each choice situation in a transport network has different characteristics and possibilities, also depending on the current status of the transport network. In this regard, travel diaries based on tracking technologies can capture precise observations for a long term. In this work, we exploit a large-scale tracking dataset, collected through a mode detection algorithm, to understand route choices of public transport users. We propose a choice set generation algorithm, able to cover more than 94% of the collected trips without any computational constraint. We compare the users’ paths in the public transport network with different choice sets, under multiple performance indicators, including coverage, size, and fit. This latter is computed by the estimation of a Path Size Logit model. The use of Automatic Vehicle Location (AVL) data allows comparing the available paths in terms of public transport vehicles used. We also consider different information provisions of network conditions and disturbances (full knowledge, no knowledge and current knowledge), and study which information provision best represents the choice set inferred by the observed users’ behaviour. Estimating a Mixed Path Size Logit model, we identified high heterogeneity among the users in only a few aspects. Overall, a condition of no knowledge results as the best fit, i.e. users seem to take into account in a minor way the realized delays in the alternatives considered when deciding their public transport route.

Numerical Stability of Conservation Equation for Bus Travel Time Prediction Using Automatic Vehicle Location Data

Travel time is a variable that varies over both time and space. Hence, an ideal formulation should be able to capture its evolution over time and space. A mathematical representation capturing such variations was formulated from first principles, using the concept of conservation of vehicles. The availability of position and speed data obtained from GPS enabled buses provide motivation to rewrite the conservation equation in terms of speed alone. As the number of vehicles is discrete, the speed-based equation was discretized using Godunov scheme and used in the prediction scheme that was based on the Kalman filter. With a limited fleet size having an average headway of 30 min, availability of travel time data at small interval that satisfy the requirement of stability of numerical solution possess a big challenge. To address this issue, a continuous speed fill matrix spatially and temporally was developed with the help of historic data and used in this study. The performance of the proposed Advanced Time-Space Discterization (AdTSD) method was evaluated with real field data and compared with existing approaches. Results show that AdTSD approach was able to perform better than historical average approach with an advantage up to 11% and 5% compared to Base Time Space Discretization (BTSD) approach. Also, from the results it was observed that the maximum deviation in prediction was in the range of 2–3 min when it is predicted 10 km ahead and the error is close to zero when it is predicted a section ahead i.e. when the bus is close to a bus stop, indicating that the prediction accuracy achieved is suitable for real field implementation.

Intersection traffic signal optimisation considering the impact of upstream curbside bus stops

Transit-oriented travel mode is considered as one of the most effective travel strategies for improving the level of travel service and reducing travel time and delay. Curbside bus stops are often built to support the operation of the transit system, which leads to new bottlenecks of road segments near such stops and exerts a negative impact on traffic flows of neighbouring intersections. The authors propose a traffic signal optimisation model with the objective to minimise total travel delay by considering the interaction of traffic flows between the intersections and upstream curbside bus stops. First, transits’ following and lane-changing behaviours are explored by analysing their trajectories collected by unmanned vehicle aerial video detectors. Second, transits’ occupancy time at curbside bus stops is statistically analysed by using automatic vehicle location data. They then use an equivalent-flow analysis method to quantify the impact caused by transits’ actions of lane-changing and moving into/out stop basins (including the waiting process). Such impact is integrated into the algorithm of intersection traffic signal optimisation. They finally validate the proposed signal optimisation model based on real-time traffic data collected from Nanjing, China. It is revealed that the improved model can reduce vehicle travel delay by 4.3% at the tested intersection.

Evaluation of roadway spatial-temporal travel speed estimation using mapped low-frequency AVL probe data

• Transit automatic vehicle location (AVL) data is used for travel time applications. • A practical model is proposed for predicting the arrival times. • The model can identify traffic delay at bus stops and intersections. • Experimental result demonstrate good prediction accuracy with minimum cost. The rapid increase in the number of vehicles equipped with GPS devices has resulted in using automatic vehicle location (AVL) data as probes to identify traffic flow status as well as route travel speed on a very fine spatial-temporal scale. However, these traffic monitoring approaches heavily rely on the widely distributed probe vehicles in the network and the high frequency of these probe samples, which are rarely implemented in the real world. This study aims to analyze the applicability of providing accurate traffic flow information from four types of low-frequency AVL data. Each data source is applied for speed estimation to develop guidelines on GPS data requirements for travel speed estimation. First, the probe sample size of each data source on each target corridor is studied to reveal the road segments that have the potential for speed estimation, along with the GPS sampling frequency of each data source. Second, the impact of probe vehicle types, sample sizes, and GPS sampling frequency is analyzed. This study offers guidance in using GPS data to conduct speed estimation in different scenarios, which can be further implemented in a prototype software tool for estimating the real-time travel speed. This study has shown the applicability for speed estimation from four types of GPS data, where the transit bus GPS data provides the best mean speed estimation. The speed estimation results are compared with loop detector data on a test road segment to evaluate its accuracy. The comparison results show that given the current GPS data sample size and updating frequency, the transit bus GPS data can provide a reasonably accurate estimation of the traffic flow speed with a mean absolute speed difference of 6.96 km/h.

Characteristics Analysis of Bus Stop Failure Using Automatic Vehicle Location Data

Queue forming behind a bus stop on an urban street is common and a traffic bottleneck usually occurs around the bus stop area. The bus stop failure means arriving buses cannot move into the bus stop due to limited capacity but have to wait for available loading areas. It is related with the transit operation level. Traditionally, the failure rate (FR), defined as the percentage of buses that arrives at the bus stop to find all loading areas occupied, is adopted in bus capacity analysis. However, the concept of FR is unable to quantitatively analyze failure characteristics in terms of its dispersion and uncertainty over time. Therefore, in this paper, we propose a new index called failure duration rate (FDR) to evaluate the bus stop failure, which can characterize waiting time for traffic delay calculation and capacity drop estimation. The automatic vehicle location data at eight bus stops in Wujiang District Suzhou, China, over 56 working days, are used to analyze the temporal characteristics of FR and FDR. We next examined the failed service duration characteristics during peak hours at the eight bus stops. Based on these characteristics analyses, we then proposed a Distribution Fitting and Cumulative Distribution Correlation (DF-CDC) approach to explore the correlation between FDR and FR at the same cumulative distribution function levels and validated the bus stop failure performance using the cross-validation method. The analysis results revealed that (i) FR fluctuates more significant than FDR, (ii) FDR is a more robust index than FR in describing the traffic characteristics incurred by bus stop failures, and (iii) FDR performs better in failure characteristics analysis than FR.

Factors affecting bus bunching at the stop level: A geographically weighted regression approach

Abstract Efficient operation of bus networks is vital for urban centers. Unfortunately, factors such as uneven passenger loads and congestion hinder the adherence to posted schedules, leading to reliability issues. Most notably, bus bunching has been identified as a significant reliability problem, impacting both users and operators. Bus bunching is treated as a route-level problem in the relevant literature, while spatial patterns in explanatory factors are overlooked. Diverging from the typically performed route-level analysis, this study exploits Automatic Vehicle Location data to investigate factors affecting bus bunching at the network level, while taking into account their spatial variability. For this purpose, a Geographically Weighted Regression Model is applied to model bus bunching, using bus stop and network attributes as explanatory variables. Results for approximately 360 bus stops in Athens, Greece underline the superiority of the proposed model to Ordinary Least Squares Regression and corroborate the presence of spatial variability in the factors affecting bus bunching. Indeed, the number of traffic lanes at the stop level is positively associated with bunching in heavy traffic segments, whereas a higher number of lanes is negatively linked to bunching in less congested regions. Further, the number of bunching occurrences generally increases with the number of routes serving each stop, as well as with the distance from subway stops in the outer parts of the city. Such findings highlight the need to consider spatial structures in relevant models and can help improve their reliability and accuracy.

Estimating the influence of crowding and travel time variability on accessibility to jobs in a large public transport network using smart card big data

Accessibility metrics are gaining momentum in public transportation planning and policy-making. However, critical user experience issues such as crowding discomfort and travel time unreliability are still not considered in those accessibility indicators. This paper aims to apply a methodology to build spatiotemporal crowding data and estimate travel time variability in a congested public transport network to improve accessibility calculations. It relies on using multiple big data sources available in most transit systems such as smart card and automatic vehicle location (AVL) data. São Paulo, Brazil, is used as a case study to show the impact of crowding and travel time variability on accessibility to jobs. Our results evidence a population-weighted average reduction of 56.8% in accessibility to jobs in a regular workday morning peak due to crowding discomfort, as well as reductions of 6.2% due to travel time unreliability and 59.2% when both are combined. The findings of this study can be of invaluable help to public transport planners and policymakers, as they show the importance of including both aspects in accessibility indicators for better decision making. Despite some limitations due to data quality and consistency throughout the study period, the proposed approach offers a new way to leverage big data in public transport to enhance policy decisions.

Identifying spatio-temporal patterns of bus bunching in urban networks

The objective of this paper is to identify hot spots of bus bunching events at the network level, both in time and space, using Automatic Vehicle Location (AVL) data from the Athens (Greece) Public Transportation System. A two-step spatio-temporal clustering analysis is employed for identifying localized hot spots in space and time and for refining detected hot spots, based on the nature of bus bunching events. First, the Spatio-Temporal Density Based Scanning Algorithm with Noise (ST-DBSCAN) is applied to distinguish bunching patterns at the network level and subsequently a k++means algorithm is employed to distinguish different types of bunching clusters. Results offer insights on specific time periods and route segments, where bus bunching events are more likely to occur and, also, on how bus bunching clusters change over time. Further, headway deviation analysis reveals the differences in the characteristics of the various bunching event types per line, showing that routes running on shared corridors experience more issues while underlying causes may vary per line. Collectively, results can help guide practice toward more flexible solutions and control strategies. Indeed, depending on the type of spatio-temporal patterns detected, appropriate improvements in service planning and real-time control strategies may be identified in order to mitigate their negative effects and improve quality of service. In light of emerging electric public transport systems, the proposed framework can be also used to determine preventive strategies and improve reliability in affected stops prior to the deployment of charging infrastructure.

Analysis of Bus Travel Time Variability using Automatic Vehicle Location Data

Transit service reliability is a measure of the quality of service offered by the public transit systems. The nature and pattern of the travel time variability can be described by the travel time distribution and it is the prerequisite in the reliability analysis. Many research works have been carried out to understand the travel time distribution, but there are very few studies on the heterogeneous traffic in developing countries like India. In this study, an attempt has been made to analyze the travel time distribution at different spatial and temporal aggregation scales using Automatic Vehicle Location (AVL) data of public bus route in Mysore city. The results suggest that, Generalised extreme value (GEV) distribution is the better fit for travel times at both route and segment levels and the performance of the Generalised extreme value (GEV) distribution is comparatively better in both temporal and spatial aggregation compared to other distributions. This distribution can be utilized by the transit operators in the operation and performance evaluation of transit systems.

Impact of a New Metro Line in Amsterdam on Ridership, Travel Times, Reliability and Societal Costs and Benefits

The north-south metro line in Amsterdam became operational in summer 2018, accompanied by changes to the existing bus, metro, and tram network in the city. In this paper we undertake an ex-post analysis of the transportation impacts of the network change. Using two sets of smart card transactions, of 5-6 weeks each, and corresponding Automatic Vehicle Location (AVL) data, a before-after comparison is made, concerning ridership, travel times, number of transfers, and travel time reliability. The results show a 4% increase in network wide working day ridership and a strong shift from tram and bus to metro. On an average working day, more than 6,000 hours of travel time is saved. 21% of travellers have more than 1 minute shorter travel time and 13% of travellers have more than 1 minute travel time increase. Furthermore, slightly fewer transfers are made, and the aggregated effect on travel time reliability is marginally positive. For an average working day (7am to 7pm), the resulting daily societal benefits of the new public transport network are approximately €54,200. On a yearly basis the transport related societal benefits are approximately 22 million euros. Doing an ex-post analysis is not common in the literature and in practice, and therefore in a lot of cases the realized benefits of large infrastructural investments remain unknown. This study provides an example of scientific methodology development using multiple data sources, that enables such ex-post evaluations, leading to improvements in public transport assessment and planning.

A stochastic schedule-following simulation model of bus routes

Microsimulation models of bus routes allow transit operators to both better understand the dynamics of bus routes and facilitate better policy making. Several simulation models of bus routes have been proposed in the literature, including cellular-automata, bus-following and traffic-following models. The majority of these approaches aim to simulate the interactions of a bus with other buses (the bus-following model), with passengers or the surrounding traffic (the traffic-following model), but they all fail to consider the important interactions between buses and their schedules. In a conventional schedule-based public transport system, bus drivers aim to arrive at each stop on time. This means that they will either speed up or slow down if their vehicles are not meeting the schedule. The research within this paper is a novel contribution to the literature of bus route simulation. We introduce the first schedule-following model where buses try to adhere to their schedule in a conventional schedule-based public transport system. A simulated numerical analysis shows the characteristics of the proposed schedule-following model and compares it to existing models. Finally, the model is calibrated using Automatic Vehicle Location and Smart Card data from Brisbane, Australia. The results show good model performance against the observed data. The model is relatively simple, yet the fundamental mechanisms that drive the model are novel and it has the potential to be applied in any city with well-defined bus schedules.

Using Link-Level Archived Automatic Vehicle Location Data to Assess Transit System LOS at Bus-Stop Level

The focus of this paper is on assessment of transit system level-of-service (LOS) at bus-stop level using the proposed percentage-based performance measure and compare it with the fixed-range-based performance measure. Data captured through automatic vehicle location (AVL) units installed on Charlotte Area Transit System (CATS) buses for 2012 and archived was used to develop query tools and compute link-level transit system performance measures, conduct analysis, and derive meaningful interpretations. The query tools developed computed performance measures by comparing actual bus travel time along selected links (between two consecutive fixed bus stops) with the scheduled travel time along the same link for each run in a year. Actual delay time and early arrivals as well as percentage of delays and early arrivals were computed to assist in the assessment. The analysis was conducted by time-of-the-day and day-of-the-week, for both travel directions, along selected bus routes/segments to assist in assessing the applicability of the measures. Findings from the research indicate that percentage-based performance measure is more reliable than fixed-range-based performance measure (delay or difference in travel time) for planning and assessment of operational performance by transit agencies.

On the accuracy of schedule-based GTFS for measuring accessibility

In this paper we assess the accuracy with which General Transit Feed Specification (GTFS) schedule data can be used to measure accessibility by public transit as it varies over space and time. We use archived Automatic Vehicle Location (AVL) data from four North American transit agencies to produce a detailed reconstruction of actual transit vehicle movements over the course of five days in a format that allows for travel time estimation directly comparable to schedule-based GTFS. With travel times estimated on both schedule-based and retrospective networks, we compute and compare a variety of accessibility measures. We find that origin-based accessibility even when averaged over one-hour periods can vary widely between locations. Origins with lower scheduled access tend to produce less reliable estimates with more variability from hour to hour in real accessibility, while higher access zones seem to converge on an estimate 5-15 percent lower than the schedule predicts. Such over- and under-predictions exhibit strong spatial patterns which should be of concern to those using accessibility metrics in statistical models. Momentary measures of accessibility are briefly discussed and found to be weakly related to momentary changes in real access. These findings bring into question the validity of some recent applications of GTFS data and point the way toward more robust methods for calculating accessibility.

Bus Arrival Time Modeling Based on Auckland Data

Inaccurate bus arrival time predictions are counterproductive to changing transport habits and promoting public transport use. This research sought to improve the bus passenger experience in terms of bus arrival time prediction by investigating various time series and regression-based techniques suitable for bus arrival time modeling. The models developed in the current study included: random walk with drift, multivariate linear regression, decision tree, artificial neural networks, and gene expression programming models. Historic automatic vehicle location and passenger flow data obtained for four bus routes spanning Auckland city, in both travel directions, were used as model inputs. Specifically, 10 independent variables were incorporated in the regression models, with distance between bus stops being the most significant predictor for bus travel time. Research results indicated that time series models outperformed regression techniques, with the time series artificial neural network being the most successful of the seven models developed. Moreover, the alternative models all performed significantly better than the prediction engine currently utilized by an Auckland bus company for arrival time prediction. However, these results require corroboration with manually collected field data, on account of the quality concerns afflicting the raw data reported by the transport company.

Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network

The “left-behind” phenomenon occurs frequently in Urban Rail Transit (URT) networks with booming travel demand, especially during peak hours in a complex URT network, which makes passenger travel patterns more complicated. This paper proposes a methodology to mine passenger travel patterns based on fare transaction records from automatic fare collection (AFC) systems and Automatic Vehicle Location (AVL) data from Communication Based Train Control (CBTC) Systems or tracking systems. By introducing the concept of a sequence, a space-time-sequence trajectory model is proposed to simulate a passenger’s travel activities, including when they are left-behind. The paper analyzes passenger travel trajectory links and estimates the weight of each feasible trajectory under tap-in/tap-out constraints. The station time parameters, including access/egress and transfer walking-time parameters, are important inputs for the model. The paper also presents a maximum-likelihood approach to estimate these parameters from AFC transaction data and AVL data. The methodology is applied to a case study using AFC and AVL data from the Beijing URT network during peak hours to test the proposed model and algorithm. The estimation results are consistent with the results obtained from the authorities, and this finding verifies the feasibility of our approach.

Transit performance assessment based on graph analytics

ABSTRACTGPS-equipped public transit vehicles generate a massive amount of location information, yet analytical methods based on Geographic Information System and Relational Database Management Systems are limited in their ability to handle these data for transit performance assessment. Graph analytics approach appears well suited for addressing these limitations; however, existing graph data models that have been used to represent the transit network do not provide the flexibility to incorporate mobility context from Automatic Vehicle Location (AVL) feeds with the geographic context of the network. This research work presents a new graph model that accounts for the mobility and geographical contexts of transit networks yet capable of processing a large volume of AVL data feeds for transit performance assessment. The efficacy of the proposed graph model and analytics method has been demonstrated in using simple graph queries to retrieve operational-level performance indicators such as schedule adherence, bus stops and routes activity levels.

Assigning Bus Delay and Predicting Travel Times using Automated Vehicle Location Data

The Washington Metropolitan Area Transit Authority (WMATA) operates 1,250 buses on 168 different routes between 10,600 bus stops to support around 370,000 passengers each day. Utilizing sensors on vehicles and analyzing their location and movements throughout an hour, trip, or day can provide valuable information to a transit authority as well as to the users of a transit system. This amount of information can be overwhelming, but utilizing big data techniques can empower the data and the transit agency. First, this paper develops a methodology for assessing previous delays in the system by applying big data structure and statistical analysis to the data constantly collected by WMATA buses. This method of analysis also helps quantify the impact of potential transit system improvements. Second, the paper describes a model that uses the real-time data, that represents potential delays, to provide future passengers with more accurate arrival predictions despite delays. These analyses are powerful tools for agencies and planners to assess and improve transit service performance using big data analytics and real-time predictions.

Passenger Travel Time Reliability for Multimodal Public Transport Journeys

Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Therefore, the passenger experience of travel time reliability is based on the whole journey experience including the transfers. Although the impact of transfers on reliability has been highlighted in the literature, the existing indicators either focus on unimodal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing “reliability buffer time” metric to transit journeys with multimodal transfers and develops a methodology to calculate it using a combination of smartcard and automatic vehicle location data. The developed methodology is applied to a real-life case study for the Amsterdam transit network consisting of bus, metro, and tram lines. By using a consistent method for all journeys in the network, reliability can be compared between different transit modes or between multiple routes for the same origin–destination pair. The developed metric can be used to study the reliability impacts of policies affecting multiple transit modes. It can also be used as an input to behavioral models such as mode, route, or departure time choice models.