Automatic Vehicle Location Systems Research Articles

Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data

There has been a recent push towards using opportunistic sensing data collected from sources like automatic vehicle location (AVL) systems, mobile phone networks, and global positioning system (GPS) tracking to construct origin-destination (O-D) matrices, which are an effective alternative to expensive and time-consuming traditional travel surveys. These data have numerous drawbacks: they may have inadequate detail about the journey, may lack spatial and temporal granularity, or may be limited due to privacy regulations. Taxi trajectory data is an opportunistic sensing data type that can be effectively used for O-D matrix construction because it addresses the issues that plague other data sources. This paper presents a new approach for using taxi trajectory data to construct a taxi O-D matrix that is dynamic in both space and time. The model’s origin and destination zone sizes and locations are not fixed, allowing the dimensions to vary from one matrix to another. Comparisons between these spatiotemporal-varying O-D matrices cannot be made using a traditional method like matrix subtraction. Therefore, this paper introduces a new measure of similarity. Our proposed approaches are applied to the taxi trajectory data collected from Lisbon, Portugal as a case study. The results reveal the periods in which taxi travel demand is the highest and lowest, as well as the periods in which the highest and lowest regular taxi travel demand patterns take shape. This information about taxi travel demand patterns is essential for informed taxi service operations management.

Design and Implementation of a Customable Automatic Vehicle Location System in Ambulances and Emergency Vehicle Systems.

Background:Automatic vehicle location (AVL) refers to a system that calculates the geographical location of any vehicle, i.e., latitude and longitude. Vehicle location information about one or more moving vehicles can be stored in the internal memory and accessed when vehicles are available (offline tracking). It is also possible to get location information on a real-time basis (online tracking). The real-time tracking systems designed to date may incorporate three devices: global positioning system (GPS), geographic information system, and cellular communication platforms that may be either a general packet radio service (GPRS) or any private and local radiofrequency network.Methods:The GPS-based navigation system has been designed so as to allow for user-friendly real-time tracking applications for any emergency vehicles like ambulances. First, GPS coordinates are obtained from the SIM908 module and sent via to a server transmission control protocol/internet protocol. Server codes, which are written in C#, load Google map to show real-time location.Results:We designed online tracking AVL hardware in the two simple and advanced versions. The latter enables both the ambulance driver and the data center to monitor path real-time besides enabling the vehicle driver to receive and make calls and send or receive messages. The former only sends latitude and longitude to the data server continuously, and the path travelled by vehicle is displayed.Conclusion:SIM908 integrates GSM, GPRS, and GPS in one package. It can be a proper choice for real-time economic tracking systems despite its low accuracy in finding geolocations.

Applying Spatial Aggregation Methods to Identify Opportunities for New Bus Services in London

Innovative analyses of origin–destination (OD) data derived from automatic fare collection and automatic vehicle location systems in public transport networks enable planners to gain new insights into how passengers travel in the network and the quality of service provided, and can even inform decisions about network improvements. Particularly in large, complex networks, systematic, data-driven approaches to network evaluation and planning are essential. New methodologies are needed to transform OD data into informative metrics and planning recommendations. This paper proposes a framework for this process and applies it to London’s public transport network. Though there are many ways to improve public transport networks, this paper focuses on the addition of new bus routes to reduce circuity. The proposed framework includes three steps that combine OD-level analysis with spatial aggregation methodologies for the identification of corridors for new bus services. First, bus stops and rail stations were clustered into geographic zones. Second, a subset of zonal OD pairs with circuitous service were identified as candidates for improvement through new bus routes, based on performance standards established with user-defined parameters. Third, an algorithm that clusters OD pairs into corridors was applied to identify promising corridors for new bus services. This paper discusses corridors identified for new services in the London case study.

Subway Service Down Again? Assessing the Effects of Subway Service Interruptions on Local Surface Transit Performance

Cities around the world are keen to offer modern urban transit systems that connect various locations in an efficient and reliable manner with the highest degrees of riders’ experience. These systems may consist of various modes such as buses, streetcars (or trams), and rapid transit systems (e.g., subways). In both research and practice, the quality of transit service has traditionally been measured and investigated on a mode-by-mode basis. Therefore, it is rare to find studies that investigate the impacts of poor performance or breakdown of one transit mode on other functioning modes in multimodal integrated transit systems. This research aims at understanding the impact of subway service interruptions on the speed performance of surface transit in Toronto, Ontario. To do that, a detailed data set of subway service interruptions collected in 2013 by the Toronto Transit Commission (TTC), the public transit provider in the City of Toronto, was used. In addition, another data set was obtained from the TTC’s automatic vehicle location system for 51 bus and streetcar routes that are within a short walking distance of some subway stations. Using two statistical models, the paper results indicate that subway service interruptions have a statistically significant negative impact on bus and streetcar service operations in terms of slower speeds, with more immediate and intense impacts on streetcar service.

On Individual Repositioning Distance along Platform during Train Waiting

Out of waiting times spent in rail stations on boarding platforms, some part can be reinvested by the trip-makers to optimize their positions of boarding and save on travel time for the rest of their trips. This paper provides a stochastic model, in which user’s journey is decomposed into phases of, successively, walking in the access station, platform positioning, waiting for boarding, train riding, and walking in the egress station. Walking speed and target position are modeled as individual factors, and in-station distances as random variables. Service timetable is exogenous. This makes egress times and exit instants random variables that are characterized by distribution and mass probability functions under closed-forms, for both single and distributed walking speeds. Specific statistical distributions are shown to ease computation. The resulting PDF formulae make likelihood functions of the model parameters. Maximum likelihood estimation is proposed and applied to a case study of commuter rail line in Paris: journeys between stations Vincennes and La Défense along line A of the Regional Express Railways. Based on data from Automated Fare Collection and Automatic Vehicle Location systems and pertaining to an individual user, satisfactory results were obtained.

Extracting bus transit boarding stop information using smart card transaction data

The smart card-based automated fare collection (AFC) system has become the main method for collecting urban bus and rail transit fares in many cities worldwide. Such smart card technologies provide new opportunities for transportation data collection since the transaction data obtained through AFC system contains a significant amount of archived information which can be gathered and leveraged to help estimate public transit origin–destination matrices. Boarding location detection is an important step particularly when there is no automatic vehicle location (AVL) system or GPS information in the database in some cases. With the analysis of raw data without AVL information in this paper, an algorithm for trip direction detection is built and the directions for any bus in operation can be confirmed. The transaction interval between each adjacent record will also be analyzed to detect the boarding clusters for all trips in sequence. Boarding stops will then be distributed with the help of route information and operation schedules. Finally, the feasibility and practicality of the methodology are tested using the bus transit smart card data collected in Guangzhou, China.

Smartphone‐based crowdsourcing for position estimation of public transport vehicles

In this research, a real-time positioning method, which utilises crowdsourced positioning data obtained from smartphone GPS is developed. Such vehicle location information obtained from crowdsourcing and smartphones in public transport could replace traditional automatic vehicle location systems. However, the location information from smartphone GPS is more erroneous. The proposed methodology serves as an alternative to existing positioning methods to improve the vehicle positioning accuracy. The developed enhanced particle filter algorithm takes smartphone GPS positioning data [from multiple passengers in a single transit vehicle (e.g. bus)] as input data. This `crowdsourced' data can then be utilised to calculate the vehicles' positioning information with better accuracy using the developed enhanced particle filter algorithm. The developed algorithm was tested using data collected on 14 different bus routes in urban and suburban areas of Mumbai, India, and it was identified that the algorithm is effective in reducing the average error up to 21.3% from a regular smartphone GPS and 10% from extended Kalman filter algorithm and was able to curtail positioning error within 8.672 m (average over 14 routes).

An Offline Framework for the Diagnosis of Time Reliability by Automatic Vehicle Location Data

Time reliability problems are unavoidable, owing to the stochastic context in which bus services are operated. Therefore, characterizing their reliability and understanding possible sources of unreliability provides an opportunity to keep buses on schedule and/or maintain planned headways. Measuring time reliability is technologically feasible by automatic vehicle location (AVL) systems, which can collect disaggregated data on the delivered service and disclose information on its performance. This paper proposes the first offline framework applicable to any bus route in order to accurately characterize the bus stops and the time periods in which reliability is insufficient, and to disclose the systematic unreliability sources from collected AVL data and select preventive strategies, accordingly. The framework is tested on the real case study of a bus route, using about 40 000 AVL data records provided by the bus operator CTM in Cagliari, Italy. The experimentation shows that this framework can be adopted by transit managers for accurate reliability analysis.

Managing Data and Rethinking Applications in an Innovative Mid-sized Bus Fleet

Abstract Nowadays, Automatic Vehicle Location (AVL) systems are being used extensively in worldwide cities to improve efficiency, effectiveness and quality of road transit services. Taking the up-to-date Cagliari's AVL system as a case study, the paper looks at the uses of real-time and not-real functions based on AVL data, disclosing some characteristics. Although the Cagliari's AVL system is similar to other systems, it is somewhat larger in terms of the scale of implementation and range of innovative applications. Therefore, its illustration is expected to provide rooms for data management and use of innovative technologies for transit experts, as well as a benchmark tool for the scientific community.

Modeling Bus Service Time for a Curbside Stop

Bus queuing occurs frequently at the entry and exit areas of curbside stops. Formed queues can induce extra delays for bus operations. Conventional regression-based models to analyze bus service time cannot capture such delays because of their limitations in addressing interactions between buses and arriving passengers. To capture the extra delays, this paper proposes a new approach to estimate bus service time on the basis of the Monte Carlo method. The proposed models account for interactions between arriving buses as well as the numbers of boarding and alighting passengers. The models were established for curbside stops with both one and two berths. Case studies were implemented to show the effectiveness of the proposed approach. Archived data from the automatic vehicle location system and the automatic fare collection system were used to calibrate and validate the models. With the established models, the impact of passenger arrivals on bus service time was further demonstrated.

Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data

Electronic fareboxes integrated with Automatic Vehicle Location (AVL) systems can provide location-stamped records to infer passenger boarding at individual stops. However, bus loads and Origin-Destination (OD) flows, which are useful for route planning, design, and real-time controls, cannot be derived directly from farebox data. Recently, Wi-Fi sensors have been used to collect passenger OD flow information. But the data are insufficient to capture the variation of passenger demand across bus trips. In this study, we propose a hierarchical Bayesian model to estimate trip-level OD flow matrices and a period-level OD flow matrix using sampled OD flow data collected by Wi-Fi sensors and boarding data provided by fareboxes. Bus loads on each bus trip are derived directly from the estimated trip-level OD flow matrices. The proposed method is evaluated empirically on an operational bus route and the results demonstrate that it provides good and detailed transit route-level passenger demand information by combining farebox and Wi-Fi signal data.

Real-Time Bus-Holding Control Strategy to Reduce Passenger Waiting Time

This paper presents research on a real-time bus-holding control strategy that minimizes passenger waiting time. This bus-holding strategy forces buses to hold at stations for a while after a passenger exchange is finished. A mathematical model is proposed to determine the optimal holding time. Both onboard and on-station passenger waiting times have been taken into account. Given the real-time nature of the problem, a heuristic method based on gradient descent algorithms was developed. The proposed control strategy was evaluated by using data derived from a shuttle bus rapid transit (BRT) line in the city of Mashhad, Iran. The BRT line was simulated and calibrated by available empirical and real-time data from the automatic vehicle location and automatic passenger counting systems. The results indicate that the proposed bus-holding strategy reduces total passenger waiting time by 8.65%.

Quantile Regression Analysis of Transit Travel Time Reliability with Automatic Vehicle Location and Farecard Data

Transit agencies increasingly deploy planning strategies to improve service reliability and real-time operational control to mitigate the effects of travel time variability. The design of such strategies can benefit from a better understanding of the underlying causes of travel time variability. Despite a significant body of research on the topic, findings remain influenced by the approach used to analyze the data. Most studies use linear regression to characterize the relationship between travel time reliability and covariates in the context of central tendency. However, in many planning applications, the actual distribution of travel time and how it is affected by various factors is of interest, not just the condition mean. This paper describes a quantile regression approach to analyzing the impacts of the underlying determinants on the distribution of travel times rather than its central tendency, using supply and demand data from automatic vehicle location and farecard systems collected in Brisbane, Australia. Case studies revealed that the quantile regression model provides more indicative information than does the conditional mean regression method. Moreover, most of the coefficients estimated from quantile regression are significantly different from the conditional mean–based regression model in terms of coefficient values, signs, and significance levels. The findings provide information related to the impacts of planning, operational, and environmental factors on speed and its variability. On the basis of this information, transit designers and planners can design targeted strategies to improve travel time reliability effectively and efficiently.

Perceived Reality

Ensuring that customers are satisfied with public transit is important, and traditionally transit agencies have assessed customer satisfaction by using questionnaires designed to collect information about users’ personal characteristics and perceptions of service. However, these questionnaires assess only individuals’ perceptions of transit services, without accounting for the service that users actually experienced. With that in mind, the purpose of this paper is to analyze the drivers of public transit satisfaction for users on the basis of an analysis of customer satisfaction questionnaires, as well as operations data obtained from automatic vehicle location and automatic passenger counter systems for an express bus route in Vancouver, British Columbia, Canada. The goal of the paper is to understand what the main factors influencing customer satisfaction in this context are. The paper questions whether using operations data in parallel with passengers’ perception data is useful in understanding customer satisfaction. With a series of logit models, it is found that actual crowding and users’ reported satisfaction with crowding are associated with how transit users perceive overall satisfaction with the bus service. Furthermore, the models reveal that car access, age, past use, and users’ perceptions of frequency, onboard safety, and cleanliness are also positively associated with overall satisfaction. This study could be useful for public transit planners as it provides new insight into how data derived from customer satisfaction surveys and bus operations can be used to identify which modifiable components of the service can be prioritized to effectively increase riders’ overall satisfaction.

Identifying automatic vehicle location (AVL) data completeness issues in a rural transit authority system

We analyzed AVL stop level data from a rural transit system to identify data completeness and systematic data capture failures. Systematic data loss could compromise the validity of further analyses of the data, such as schedule adherence or run time performance. We audited the data to identify missing values and possible data recording errors. The frequency of missing values was analyzed as a function of trip start time, stop number, day of the week, and last reported seconds late. We also perform an outlier and extreme value analysis as a function of missing records per trip. We conclude that there are systematic data capture errors in the system that needs to be addressed before further studies, such as run time analysis can be performed. Given the widespread adoption of an AVL system by rural transit system, it is recommended that detail data completeness analysis becomes routine before using the data generated to perform other studies.

Vehicle scheduling based on variable trip times with expected on‐time performance

AbstractThe vehicle scheduling problem (VSP) is concerned with determining the most efficient allocation of vehicles to carry out all the trips in a given timetable. The duration of each trip (called trip time) is normally assumed to be fixed. However, in practice, the trip times vary due to the variability of traffic, driving conditions, and passengers’ behavior. It is therefore difficult to adhere to the compiled schedule. This paper proposes a new VSP model based on variable trip times. Instead of being a fixed value, the duration of a trip falls into a time range that can be easily set based on the schedulers’ experience or the data collected by automatic vehicle location systems. Within this range, an expected trip time is provided according to the schedulers’ expectation of on‐time performance. This new model can reduce schedulers’ pressure on setting fixed scheduled trip times. Moreover, computational results generated using CPLEX show that this model can increase the on‐time performance of resulting schedules without increasing the fleet size.

Have they bunched yet? An exploratory study of the impacts of bus bunching on dwell and running times

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

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.

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.

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.