Automatic Vehicle Location Research Articles

In Pursuit of the Happy Transit Rider: Dissecting Satisfaction Using Daily Surveys and Tracking Data

This paper demonstrates the power and value of connecting satisfaction surveys from public transportation passengers to smartphone tracking data and automatic vehicle location (AVL) data. The high resolution of the smartphone location data allows travel times to be dissected into their individual components, and the connection with AVL data provides objective information on personal-level experiences of the respondents. Analyses show how these data can provide a quantitative understanding of the relationship between planned and provisioned service, and customer satisfaction. In-vehicle travel time data from 2,403 trips made by 529 unique participants could be obtained, along with origin wait time data for 779 of the trips and transfer time data for 188 trips. The addition of unreliability to the measurement of travel times, which is enabled by the highly detailed tracking data, shows that the relationship between passenger satisfaction and experienced travel times may be more nuanced than has previously been acknowledged. Ordinal logit model estimation results show a strong sensitivity of passenger satisfaction toward in-vehicle delays, and show that delays on board metro trains are perceived as more onerous than delays on board buses. This study also reveals the importance of obtaining a general measurement of satisfaction with transit service when repeated satisfaction measurements are conducted with respect to individual experiences. A baseline satisfaction level and a variable component as a function of experiences can be observed in the model results. Furthermore, the survey data include a measure of subjective well-being, which is a relatively new element in travel surveys. Insights are presented on the importance of this potential new covariate for future survey designs.

Performance Evaluation of Bus Routes Using Automatic Vehicle Location Data

Real survey data are of considerable significance for transit planners and operators to assess the performance of a bus route and then improve its level of service. Automatic vehicle location (AVL) system is a convenient tool to collect a large amount of real data from buses. This paper aims to propose a methodology to evaluate the operational performance of a bus route based on AVL data. First, several statistical indexes are selected for the evaluation, including percentile travel times, coefficient of variation (COV) of travel times, and average commercial speed and travel time distribution. Moreover, spatial and temporal features of travel time variation and transit regulation indexes are analyzed. Then the bus route with transit signal priority and dedicated bus lane in Suzhou, China, is taken as a case study to validate the proposed methodology. Numerical tests indicate that the most influential feature of travel time is its spatial and temporal patterns, which vary across segments and time-of-day intervals. Bus lane violation and route repetition may undermine the efficiency of priority measures. In addition, the schedule design has important impacts on the adherence and headway regularity.

Unifying Time Reference of Smart Card Data Using Dynamic Time Warping

The wide use of smart card automatic fare collection (AFC) systems in public transit makes it popular to analyse public transit user behaviour based on smart card data. Most smart-card-based researches are time-related, but smart card data time is not always reliable because smart card systems are usually off-line and lack of maintenance in China, and smart card data of different buses often shares different time references. This paper explores the application of dynamic time warping to unify time reference of smart card data. Based on the analysis of the relationship between boarding time in smart card data and arrival-departure time in automatic vehicle location (AVL) data, a smart card data time reference unification algorithm was proposed. The results indicate that the algorithm can work out reliable time offsets by numerical calculation and runs well with integral automatic vehicle location data. Due to the fact that the time reference of smart card data for each bus changes daily, the best way to put an end to smart card time reference problem is to make smart card systems online, such as combining smart card readers with automatic vehicle location systems.

Transportation Planning Aspects of a Smart City–Case Study of GIFT City, Gujarat

Gujarat International Finance Tec-City Company Limited (GIFTCL) is developing a financial Central Business District (CBD) namely GIFT City between Ahmedabad and Gandhinagar as a global financial and IT services hub, a first of its kind in India. GIFT City is being developed as a high quality commercial zone along with an ideal blend of residential and social facilities that optimise land and real estate values with global connectivity and generation next infrastructure. GIFT City transport master plan aims at a transit oriented development based on walk to work approach, pedestrian friendly infrastructure with zero fatal accidents, easy and fast mobility with minimum conflicts and efficient public transport systems which reduce the per capita energy basket. In accordance with the GIFT City transport vision, the transport infrastructure has been planned to cater to the daily demand as well as that of the peak hour and Multi Level Parking (MLP) lots have been proposed at suitable locations in the GIFT city. To enhance the mobility and safety in GIFT city, various smart transport applications like advanced traveller information system, security and surveillance system, intelligent parking management system, parking guiding system, automatic vehicle location and tracking, real time travel response etc are planned. GIFT City transport master plan addresses the needs of all users of GIFT City, thereby it can be called as “User-Centred Smart Transport System” and its makes it possible for all GIFT City users to enjoy a world class life style in this vibrant global city. GIFT City smart transport planning parameters and design details are elaborated in this paper.

Estimation of Short-Term Bus Travel Time by Using Low-Resolution Automated Vehicle Location Data

Estimation of short-term bus travel time is an essential component of effective intelligent transportation systems (ITS), including traveler information systems and transit signal priority (TSP) strategies. Several technologies, such as automated vehicle location (AVL) systems, can provide real-time information for estimation of bus travel time. However, low resolution of data from such technologies presents a challenge to accurate estimation of travel time. Several models for estimation of bus travel time at signalized urban arterials were developed and tested. These models used low-frequency AVL data and required only knowledge of network specifications such as locations of bus stops and intersections. First, a linear regression model was developed; it decomposed total travel time into its components, including running travel time, dwell time at bus stops, and delay at signalized intersections. Second, various machine learning models, including support vector regression (SVR) with nonlinear kernel, ridge, Lasso, decision tree, and Bayesian ridge were trained by using Python libraries such as scikit-learn and evaluated. A segment of Washington Street in Boston, Massachusetts, was selected as the study site. The results indicate that the SVR model outperformed other regression models in generalized error measures, in particular those of mean absolute error and root mean square error. The findings of this study can lead to improved traveler information systems and more-efficient TSP strategies and, overall, can contribute to better transit quality of service.

Perspectives on Transit: Potential Benefits of Visualizing Transit Data

Advancements in information and communication technologies have enabled transit agencies around the world to generate streams of data on a high-frequency basis. Increasingly, these agencies are interested in new methods of visualizing these data to communicate the results of their planning efforts, operational investments, and overall transit performance to decision makers and stakeholders. Most agencies today collect and provide numerous kinds of data, including Google’s general transit feed specification schedule data, automatic vehicle location data, and automatic passenger count data. This paper aims to demonstrate the untapped potential of these data sources; specifically, the paper uses transit data from Montreal, Quebec, Canada, to generate performance measures that are of interest to both transit planners and marketing professionals. Some of these measures can also help in communicating the positive attributes of public transportation to the community. Performance measures are generated at different scales, including transit system, neighborhood, route, and stop levels. This paper expands on previous research on transit performance research and visualization by adopting currently available resources for so-called big data.

Passenger Journey Destination Estimation From Automated Fare Collection System Data Using Spatial Validation

A methodology for estimating the destination of passenger journeys from automated fare collection (AFC) system data is described. It proposes new spatial validation features to increase the accuracy of destination inference results and to verify key assumptions present in previous origin–destination estimation literature. The methodology applies to entry-only system configurations combined with distance-based fare structures, and it aims to enhance raw AFC system data with the destination of individual journeys. This paper describes an algorithm developed to implement the methodology and the results from its application to bus service data from Porto. The data relate to an AFC system integrated with an automatic vehicle location system that records a transaction for each passenger boarding a bus, containing attributes regarding the route, the vehicle, and the travel card used, along with the time and the location where the journey began. Some of these are recorded for the purpose of allowing onboard ticket inspection but additionally enable innovative spatial validation features introduced by the methodology. The results led to the conclusion that the methodology is effective for estimating journey destinations at the disaggregate level and identifies false positives reliably.

Data driven improvements in public transport: the Dutch example

Due to reduced budgets, higher political expectations and increasing competition between operators, there is growing pressure on public transport companies and authorities to improve their operational efficiency. It is thus of utter importance for them to be able to identify inefficiencies, bottlenecks and potentials in their public transport service. Recorded operational data, which has quickly become more widespread in the last decade, aids greatly in this process and enables operators and authorities to continually improve. In this paper we identify some of the arising possibilities. We first describe the state of publicly available transit data, with an emphasis on the Dutch situation. Next, the value of insights from Automatic Vehicle Location data is demonstrated by examples. Thereafter, a software tool is presented that enables operators and authorities to quickly perform comprehensive operational analyses, and which was able to identify several bottlenecks when applied in practice.

Validation of Automatic Vehicle Location Data in Public Transport Systems

Performance metrics for public transport systems can be calculated from automatic vehicle location (AVL) data but data collection subsystems can introduce errors into the data which would invalidate these calculations, giving rise to misleading conclusions. In this paper we present a range of methods for visualising and validating AVL data before performance metrics are computed. We illustrate our presentation with the specific example of the Lothian Buses Airlink bus, a frequent service connecting Edinburgh city centre and Edinburgh airport. Performance metrics for frequent services are based on headways, the separation in space and time between subsequent buses serving a route. This paper provides a practical experience report of working with genuine vehicle location data and illustrates where care and attention is needed in cleaning data before results are computed from the data which could incorrectly reflect the true level of service provided.

Improving Mass Transit Operations by Using AVL-Based Systems: A Survey

Intelligent transportation systems based on automated data collection frameworks are widely used by the major transit companies around the globe. This paper describes the current state of the art on improving both planning and control on public road transportation companies using automatic vehicle location (AVL) data. By surveying this topic, the expectation is to help develop a better understanding of the nature, approaches, challenges, and opportunities with regard to these problems. This paper starts by presenting a brief review on improving the network definition based on historical location-based data. Second, it presents a comprehensive review on AVL-based evaluation techniques of the schedule plan (SP) reliability, discussing the existing metrics. Then, the different dimensions on improving the SP reliability are presented in detail, as well as the works addressing such problem. Finally, the automatic control strategies are also revised, along with the research employed over the location-based data. A comprehensive discussion on the techniques employed is provided to encourage those who are starting research on this topic. It is important to highlight that there are still gaps in AVL-based literature, such as the following: 1) long-term travel time prediction; 2) finding optimal slack time; or 3) choosing the best control strategy to apply in each situation in the event of schedule instability. Hence, this paper includes introductory model formulations, reference surveys, formal definitions, and an overview of a promising area, which is of interest to any researcher, regardless of the level of expertise.

통합 연료전지모니터링 시스템 개발

The interest of New Renewable Energy is increasing globally because of the increment of the uncertainty for the energy's supply and demand, and the increment of the frequency in weather anomaly and its damages. One of the New Renewable Energies, Hydrogen receives attention as the future energy that can deal with global environment regulation. Fuel Cell Electric Vehicle (FCEV) is an environment-friendly vehicle that uses Hydrogen as fuel. The electric power for FCEV is generated by chemical reaction with Oxygen from the air and Hydrogen. Hyundai Motor Company (HMC) has developed a proprietary fuel cell system since 2005. In 2012, HMC is the first car maker that mass-produces the ix35 FCEV to the worldwide such as North America, Europe, etc. In order to develop and improve the FCEV technology, data acquisition and analysis of the driving vehicle information is essential. Therefore, the monitoring system is developed, which is consist of datalogger, Automatic Vehicle Location (AVL) server and main server. Especially, WCDMA technology is integrated into the system which enables the data analysis without any restriction of time and region. The main function of the system is the analysis of the driving pattern and the component durability, and the safety monitoring. As a result, ix35 FCEV has successfully developed by using the developed monitoring system. The system is going to take an advantage of development in the future FCEV technology.

The Dallas patrol management experiment: can AVL technologies be used to harness unallocated patrol time for crime prevention?

To examine whether information on where the police patrol drawn from automatic vehicle location (AVL) systems can be used to increase the amount of directed patrol time at high-crime police beats and crime hot spots, and whether such increases would lead to reductions in crime. In an experimental study with a block-randomized design, 232 police beats were randomly allocated to an experimental or control condition. In the experimental condition, the police commanders knew the amount of time that police spent in beats and crime hot spots. This information was not provided to commanders in the control condition. Over a 13-week period, assigned patrol time, unallocated patrol time, total patrol time, and crime were tracked at both police beats and crime hot spots (N = 1006). Knowledge of where police officers patrolled did not affect directed patrol at the beat level. At the hot spots level, the treatment group experienced meaningful increases in unallocated patrol time and total patrol time, and a decrease in crime. A key finding of the study is that information generated from AVL can be used to increase directed patrol time at crime hot spots, and that these increased levels of patrol will lead to reductions in crime. At the same time, our study points to the fact that only a small proportion of unallocated time in Dallas is actually focused on hot spots policing. We suggest that this is the reason why crime went down significantly at the hot spots but not in beats overall in Dallas.

Modeling distributions of travel time variability for bus operations

SummaryBus travel time reliability performance influences service attractiveness, operating costs, and system efficiency. Better understanding of the distribution of travel time variability is a prerequisite for reliability analysis. A wide array of empirical studies has been conducted to model distribution of travel times in transport. However, depending on the data tested and approaches applied to examine the fitting performance, different conclusions have been reported. This paper aims to specify the most appropriate distribution model for the day‐to‐day travel time variability by using a novel evaluation approach and set of performance measures. Two important issues are explored using automatic vehicle location data collected on two typical bus routes over 6 months in Brisbane, namely, data aggregation influences on travel time distribution and comprehensive evaluation of performance of distribution models. The decrease of temporal aggregation of travel times tends to increase the normality of distributions. The spatial aggregation of link travel times would break up the link multimodality distributions for a busway route, but unlike for a non‐busway route. The Gaussian mixture models are evaluated as superior to its alternatives in terms of fitting accuracy, robustness, and explanatory power. The reported distribution model shows promise to fit travel times for other services with different operation environments considering its flexibility in fitting symmetric, asymmetric, and multimodal distributions. The improved statistic fitting can support more effective service reliability analysis. Copyright © 2015 John Wiley & Sons, Ltd.

Development of Simulation Model to Improve Bus Service Reliability at High-Frequency Operation

The quality of service provided to passengers in high-frequency bus routes can be affected by variety of factors. According to this fact that high-frequency bus routes have high passenger demand and short headways (less than 10 minutes), interaction between buses would be much higher, causing decline in reliability of service. In order to represent a busy bus operation in Kuala Lumpur city centre network, a simulation model will be calibrated. The calibration and validation parameters for the key bus route will derive from data captured by the Automatic Vehicle Location (AVL) and Automatic Fare Collection (AFC) system and procedures will be outlined to adapt the model to other bus routes. The study is in its initial stage to find out what factors have highest impact on reliability of high-frequency bus services in the city centre. An initial model was developed for forecasting passenger demand by using RSM. The simulation model will first be used to conduct a sensitivity analysis of the factors influencing reliability, such as passenger demand, terminal departure behaviour, and unfilled trips. Next, several operating strategies, including terminal departure and time-point holding will be modelled and evaluated for their potential to improve reliability. Model results show that which strategies and factors can significantly improve bus service reliability.

Unreliability effects in public transport modelling

Nowadays, transport demand models do not explicitly evaluate the impacts of service reliability of transit. Service reliability of transit systems is adversely experienced by users, as it causes additional travel time and unsecure arrival times. Because of this, travellers are likely to perceive a higher utility from more reliable transport systems. In order to mimic and measure the impacts of service reliability on a transit demand model a three-step approach is proposed using automated vehicle location and smart card data. The approach consists of determining the probabilistic distribution of transit trip times, defining demand patterns and estimating the average impacts of unreliability per passenger. This approach was successfully tested on the model of the city of Utrecht in The Netherlands. By adding service reliability as a variable parameter of transit systems the results of the demand model improved, showing that the absolute difference between the observed and the estimated demand decreased by 18%. In addition, the proposed approach allows measuring the effects of expected changes in level of service reliability on traveller behaviour. Finally, we identify future research topics required to improve the estimation of those effects.

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).

Signal Progression Impacts on Transit Buses As Travel Time Probes

AbstractWith the deployment Automatic Vehicle Location systems to monitor transit vehicles (and other fleet vehicles), there have been several efforts in recent years that seek to leverage these data to also obtain arterial travel times for the private vehicle population. The fleet vehicle data capture the traffic conditions, but they also capture behavior unique to the fleet, e.g., servicing passengers at a bus stop. There are several strategies used in conventional practice to eliminate the biases that occur in the vicinity of the bus stop. While investigating the benefits of using a perception sensor to identify ambient traffic conditions around the bus and correct for transit operations, the present work revealed that even a perfect correction for local conditions resulted in large deviation between the travel time measured from the bus and the actual travel time experienced by the surrounding private vehicles. This research uncovered a fundamental issue affecting almost all systems that use buses as ...

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