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Reliability is a key factor in public transportation systems, significantly influencing passenger satisfaction and their perceptions of service quality. Therefore, measures of Travel Time Reliability (TTR), which help quantify unexpected delays, are essential for effectively planning and managing travel times. This research aims to provide a prediction model for standard deviation of travel time, as an indicator of TTR, on an urban bus route using Automatic Vehicle Location data as well as evaluating and comparing the results of ARIMA and LSTM models. Our research contributes to the existing body Ofliterature by considering the effect of a wide range of predictor variables which were categorized by route characteristics, weather condition, congestion, and temporal feature, on bus TTR, which received less attention in previous studies. The results show that LSTM is an efficient and accurate TTR predictive model than ARIMA, with an accuracy of about 87%. In addition, the LSTM model exhibits lower mean absolute percentage error (40%), mean square error (52.08%), and root mean square error (39.70%) compared to the corresponding values in the ARIMA model. Also, the congestion, weather condition, and holidays are the key variables in increasing the accuracy of LSTM model, respectively. Our findings provide insights to facilitate the decision-making of managers and planners in public transportation planning sector to improve transit reliability and passengers' satisfaction level. [ABSTRACT FROM AUTHOR]

Travel time of buses, a major part of the urban public transit system, is affected by various factors and foremost are the bus stops. In addition to the dwell time at the stop, the deceleration and acceleration zones reduce average speed, particularly in mixed traffic, and increase travel time. Several approaches are used in estimating the link-based travel time of public transit systems in transportation planning, but Bus Stop Influence Zones (BSIZs) are ignored. Moreover, fuel consumption and pollutant emissions increase in BSIZs, and this demonstrates the importance of BSIZs in measuring the performance of public transit systems as well as planning. Data obtained from the Automatic Vehicle Location system was used and visualized on Geographic Information System and data preprocessing steps were performed. Finally, changepoint detection method of Facebook Prophet (FBP-CDM) was exploited to identify changepoints in location-speed data on the selected route. Results were validated with real-life data and expert opinion, and then compared with the findings acquired by the K-means clustering. Based on the conclusions, FBP-CDM was found quite effective in detecting and predicting BSIZs accurately and the proposed methodology is useful for studies in transportation planning and operations. [ABSTRACT FROM AUTHOR]

Abstract Improving bus travel time reliability can attract more commuters to use bus transit, and therefore reduces the share of cars and alleviates traffic congestion. This paper formulates a new bus travel time reliability metric that jointly considers two stochastic processes: the in-stop waiting process and in-vehicle travel time process, and the bus travel time reliability function is calculated by the convolution of independent events’ probabilities. The new reliability metric is defined as the probability when bus travel time is less than a certain threshold and can be used in both conditions with and without bus transfer. Next, Automatic Vehicle Location (AVL) data of the city of Harbin is used to demonstrate the applicability of the proposed method. Results show that factors such as weather, day of the week, departure time, travel distance, and the distance from the boarding stop to the bus departure station can significantly affect the travel time reliability. Then, a case with low bus departure frequency is analyzed to show the impact of travelers’ arrival distribution on their bus travel time reliability. Further, it is demonstrated that the travel time reliabilities of two bus transfer schemes of the same Origin–Destination (O–D) pair can have significantly different patterns. Understanding the bus travel time reliability pattern of the alternative bus routes can help passengers to choose a more reliable bus route under different conditions. The proposed bus travel time reliability metric is tested to be sensitive to the effect of different factors and can be applied in bus route recommendation, bus service evaluation, and optimization.

Bus bunching is a common phenomenon caused by irregular bus headway, which increases the passenger waiting time, makes the passenger capacity uneven, and severely reduces the reliability of bus service. This paper clarified the process of bus bunching formation, analyzed the variation characteristics of bus bunching in a single day, in different types of periods, and at different bus stops, then concluded twelve potential factors. A hybrid model integrating a genetic algorithm with elitist preservation strategy (eGA) and decision tree (DT) was proposed. The eGA part constructs the model framework and transforms the factor identification into a problem of selecting the fittest individual from the population, while the DT part evaluates the fitness. Model verification and comparison were conducted based on real automatic vehicle location (AVL) data in Shenzhen, China. The results showed that the proposed eGA–DT model outperformed other frequently used single DT and extra tree (ET) models with at least a 20% reduction in MAE under different bus routes, periods, and bus stops. Six factors, including the sequence of the bus stop, the headway and dwell time at the previous bus stop, the travel time between bus stops, etc., were identified to have a significant effect on bus bunching, which is of great value for feature selection to improve the accuracy and efficiency of bus bunching prediction and real-time bus dispatching. [ABSTRACT FROM AUTHOR]

Improving bus travel time reliability can attract more commuters to use bus transit, and therefore reduces the share of cars and alleviates traffic congestion. This paper formulates a new bus travel time reliability metric that jointly considers two stochastic processes: the in-stop waiting process and in-vehicle travel time process, and the bus travel time reliability function is calculated by the convolution of independent events' probabilities. The new reliability metric is defined as the probability when bus travel time is less than a certain threshold and can be used in both conditions with and without bus transfer. Next, Automatic Vehicle Location (AVL) data of the city of Harbin is used to demonstrate the applicability of the proposed method. Results show that factors such as weather, day of the week, departure time, travel distance, and the distance from the boarding stop to the bus departure station can significantly affect the travel time reliability. Then, a case with low bus departure frequency is analyzed to show the impact of travelers' arrival distribution on their bus travel time reliability. Further, it is demonstrated that the travel time reliabilities of two bus transfer schemes of the same Origin–Destination (O–D) pair can have significantly different patterns. Understanding the bus travel time reliability pattern of the alternative bus routes can help passengers to choose a more reliable bus route under different conditions. The proposed bus travel time reliability metric is tested to be sensitive to the effect of different factors and can be applied in bus route recommendation, bus service evaluation, and optimization. [ABSTRACT FROM AUTHOR]

Smart card data from the Automatic Fare Collecting systems (AFC) and timetable information, such as Automatic Vehicle Location (AVL), are used in combination by practitioners and researchers to gain a deeper understanding of the public transit network. In some cases, AVL data are not available due to records being missing in the system. In such cases, people resort to the used schedule timetable such as General Transit Feed Specification (GTFS) to match smart card data to the transit network. Since delays or changes to the timetable are not contained in the scheduled timetable, it can result in wrong matches between the smart card data and the transit network. This paper shows how the uncertainty of arrival and departure times affects passengers to train assignments and proposes a method for estimating the missing arrival time of trains when the recorded timetable information is not available. The method uses the knowledge of how the tap-outs are distributed in a hierarchical, latent Bayesian model to predict the arrival times of trains. Evaluated on 15,136 train arrivals, the model can infer 70% of the arrivals times with an average error of 28 to 32 seconds depending on the station.

Bus bunching is a common phenomenon caused by irregular bus headway, which increases the passenger waiting time, makes the passenger capacity uneven, and severely reduces the reliability of bus service. This paper clarified the process of bus bunching formation, analyzed the variation characteristics of bus bunching in a single day, in different types of periods, and at different bus stops, then concluded twelve potential factors. A hybrid model integrating a genetic algorithm with elitist preservation strategy (eGA) and decision tree (DT) was proposed. The eGA part constructs the model framework and transforms the factor identification into a problem of selecting the fittest individual from the population, while the DT part evaluates the fitness. Model verification and comparison were conducted based on real automatic vehicle location (AVL) data in Shenzhen, China. The results showed that the proposed eGA–DT model outperformed other frequently used single DT and extra tree (ET) models with at least a 20% reduction in MAE under different bus routes, periods, and bus stops. Six factors, including the sequence of the bus stop, the headway and dwell time at the previous bus stop, the travel time between bus stops, etc., were identified to have a significant effect on bus bunching, which is of great value for feature selection to improve the accuracy and efficiency of bus bunching prediction and real-time bus dispatching.

Abstract Intelligent Transportation Systems (ITS) applications in public transportation have allowed for automated data collection, which is particularly useful for planning and operations. While technological advancement of ITS has so far been extensive, their usage for developing relevant planning and operational tools is rather limited. Research on planning and operations of public transportation systems has not widely investigated the potential of combining optimization models with data originating from ITS. Such applications, which could benefit from such an approach include route planning, scheduling and resource allocation in real time. In this context, this paper investigates and critically discusses potential models and methodologies in public transport planning and operations, which can benefit from ITS data, highlights their potential and identifies possible research paths on that area. The overview of literature collectively points to a series of common challenges faced by transportation professionals and underlines the need for better decision support tools for ITS data.

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.

An Adaptive Transit Signal Priority (ATSP) system is built upon and integrated with the existing Transit Automated Vehicle Location/Advanced communications system (AVL/ACS) to provide transit buses with needed priority by adaptively determining an optimum signal timing strategy based on status of both the concerned transit bus and the traffic conditions.  PATH has conducted a series of studies on ATSP in collaboration with the California Department of Transportation (Caltrans) and San Mateo County Transit District (SamTrans) since 2000, involving the development and implementation of a prototype ATSP system and studies of the deployment issues. This report summarizes a continuous research effort for refining the ATSP system through field operational tests in order to make it readily deployable.

An occurrence of congestion at a highway segment, also named bottleneck, is one of the major problems of traffic. As a result of the bottlenecks, vehicle speeds are transparently decreased and its effects continue on the traffic flow by acting upstream or downstream as an interface for a while. Thus, negative effects are observed over the capacity of the highway segment where the bottleneck occurs until traffic flow returns to normal conditions again. Therefore, traffic engineers aim to avoid this major problem in design and operation of highways. Intelligent Transportation System (ITS) is an advanced application that is used to utilize existing infrastructure more effectively instead of building new infrastructures. Today, ITS has been one of the trend study fields thanks to the development of technology and through the spread of smart cities. Automatic Vehicle Location (AVL), a powerful tool to manage fleets such as service vehicles, emergency vehicles, or public transit vehicles, is a GPS-based technology within the context of the ITS. So, agencies and organizations can follow vehicles of their fleets by utilizing satellites. In this study, an AVL dataset, integrated into public transportation systems, and Geographic Information Systems (GIS) are used to detect bottlenecks in a highway segment in Istanbul. The results showed that if the locations of bus stops and traffic signals are known, the segments, where bottlenecks occur and congestion increases, may be determined by using AVL data. Keywords: Automatic vehicle location, bottleneck detection, geographic information system, intelligent transportation system, traffic congestion. [ABSTRACT FROM AUTHOR]

The OCTA (Orange County Transit Authority) Transit Probe Project is a field operational test of an automatic-vehicle-location (AVL) system operating in Orange County, California. This report presents the final evaluation results for the project, concentrating on the operational period from June 1, 1998 to May 30, 1999. The report provides a detailed description of the system and the motivation for its design. It provides analyses of data reliability and accuracy, and analysis of the usefulness of transit probe data for predicting automobile travel times. Institutional issues are evaluated, based on interviews with involved personnel, direct observation of meetings and review of project documents. Customer surveys with bus riders and kiosk users are also documented. Bus tracking systems provide many potential benefits, helping: (1) drivers stay on schedule, (2) dispatchers respond to problems, (3) schedulers determine how much time to allocate between schedule check points, and (4) general public know when buses will arrive. Capturing these benefits requires careful planning for operational procedures, data maintenance, and system interfaces and ensuring the equipment itself it reliable. Bus tracking implementations need to involve many parties within the transportation agency, and include task assignments, data transfer methods, and strategies for using information. Because of the small size of the Transit Probe project, along with a competing project within the agency, these factors received only limited attention. As a consequence, the system was not used to a significant degree in the agency.

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. [ABSTRACT FROM AUTHOR]

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).

This paper introduces a novel transit data analytics platform for public transit planning, assessing service quality and revealing service problems in high spatiotemporal resolution for public transit systems based on Automatic Passenger Counting (APC) and Automatic Vehicle Location (AVL) technologies. The platform offers a systematic way for users and decision makers to understand system performance from many aspects of service quality, including passenger waiting time, stop-skipping frequency, bus bunching level, bus travel time, on-time performance, and bus fullness. The AVL-APC data from September 2012 to March 2016 were archived in a database to support the development of a user-friendly web application that allows both users and managers to interactively query bus performance metrics for any bus routes, stops, or trips for any time period. This paper demonstrates a case study using the platform to examine bus bunching in a transit system operated by the Port Authority of Allegheny County (PAAC) in Pittsburgh. It is found that the incidence of bus bunching is heavily impacted by the location on the route as well as the time of day, and the bunching problem is more severe for bus routes operating in mixed traffic than for bus rapid transit, which operates along a dedicated busway. Furthermore, a second case study is presented with a comprehensive analysis on a representative route in Pittsburgh under schedule changes. Suggestions for operation of this route to improve service quality are proposed based on the data analytics results. [ABSTRACT FROM AUTHOR]

In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.

While transfers extend transit service coverage by omnidirectional connections, poorly coordinated transfers significantly increase passenger waiting time, especially in case of missed connections. This paper proposes a simulation approach to investigate the feasibilities of different timed transfer strategies in both schedule planning and operational control. In particular, an Event-Based Multi-Agent Simulation (EMAS) model is proposed, that captures the interactions between the transit vehicles, its passengers and the (urban) environment by considering the transit vehicles and passengers as separate classes of agents which interact in a dynamic system. The model is validated by using observed Automatic Vehicle Location (AVL) and Automatic Fare Collection (AFC) data from two routes with transfers in South East Queensland, Australia. EMAS is then used to evaluate different timed transfer strategies for both schedule planning and operational control. The analysis on timed transfers in schedule planning provides valuable insights on the probability of missing a transfer and extra waiting time for transfer. Six different strategies for timed transfers in operational control are thoroughly tested, including an elaborate sensitivity analysis of the effectiveness of the strategies for different levels of transferring demand and schedule headways. This paper assists transit operators to exploit observed AVL and AFC data to augment the transfer coordination quality. [ABSTRACT FROM AUTHOR]

Quality is an essential aspect of public transport. In the case of regular public passenger transport by road, punctuality and regularity are criteria used to assess quality of service. Calculating metrics related to these criteria continuously over time and comprehensively across the entire transport network requires the handling of large amounts of data. This article describes a system for continuously and comprehensively monitoring punctuality and regularity. The system uses location data acquired continuously in the vehicles and automatically transferred for analysis. These data are processed intelligently by elements that are commonly used by transport operators: GPS-based tracking system, onboard computer and wireless networks for mobile data communications. The system was tested on a transport company, for which we measured the punctuality of one of the routes that it operates; the results are presented in this article. [ABSTRACT FROM AUTHOR]

The introduction in 2007 of a new public transport system in Santiago, Chile, brought to us an unexpected gift: the availability of Big Data; massive amounts of passive data obtained from technological devices installed to control the operation of buses and to administer the fare collection process. Many other cities in the world have experienced the same, and sooner or later, this is likely to happen everywhere. Seeing this opportunity, many researchers have developed tools to obtain valuable information from the available data. However, the case of Transantiago is particularly advantageous because all buses have GPS devices and the smart card presents an overall 97% penetration rate. We describe a successful experience of collaboration between academia and the public transport authority to develop tools based on passive data processing. We include a brief description of the Transantiago system and the agreements made to develop the aforementioned tools. We also describe the methods developed to obtain valuable information like public transport trips origin-destination matrices, speed profiles of buses and service quality indicators, among others. Several examples of specific uses of the information for public transport policy and planning in Santiago are presented. The paper concludes with a discussion of what else can be obtained from this data and why we believe that this can change the way we do transport planning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cairo public bus service suffers from several problems such as unscheduled stops, unscheduled route deviations, and inaccurate schedules. This research aims to study those problems for a selected bus route in Cairo using a prototype AVL system. Locations of the unscheduled stops and unscheduled route deviations, along with other trip time and speed data, were collected. Passengers' reliability on the unscheduled stops was compared to theirs on the official scheduled stops. Trip time was also modelled to assess the unscheduled stops' impact on trip time, and to check the accuracy of the scheduled trip time. Moreover, the frequency and the distance of the unscheduled route deviations, as well as the bus stops skipped during the deviations, were investigated. Lastly, solutions were proposed, using the AVL system, for the studied bus service deficiencies; and recommendations were suggested for future research. [ABSTRACT FROM AUTHOR]

The installation of an Automatic Vehicle Location (AVL) system alongside existing Automated Fare Collection (AFC) data spurred development of an inferred bus boarding and alighting ridership model at New York City Transit (NYCT), allowing for 100% passenger origin-destination (O-D) data citywide. Analysis techniques that relied primarily on professional judgment due to lack of data were replaced by more sophisticated statistical techniques. This paper describes two case studies and the resulting service planning potential from having access to fully-integrated big data sources: a neighborhood-wide analysis of performance and ridership, where 100% data allowed planners to pinpoint specific, low-cost reroutes and stop changes to better serve riders, and identification of an optimal route split location for a long route with poor performance by minimizing passenger impact using modeled O-D data. In both examples, new data sources allowed for novel analysis throughout problem investigation as well as forecasting ridership and cost impacts of proposed service adjustments. As the agency's ability to leverage these data improves, it will support Title VI obligations as well as performance monitoring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: A high-quality Origin–Destination (OD) matrix is a fundamental prerequisite for any serious transport system analysis. However, it is not always easy to obtain it because OD surveys are expensive and difficult to implement. This is particularly relevant in large cities with congested networks, where detailed zonification and time disaggregation require large sample sizes and complicated survey methods. Therefore, the incorporation of information technology in some public transport systems around the world is an excellent opportunity for passive data collection. In this paper, we present a methodology for estimating a public transport OD matrix from smartcard and GPS data for Santiago, Chile. The proposed method is applied to two 1-week datasets obtained for different time periods. From the data available, we obtain detailed information about the time and position of boarding public transportation and generate an estimation of time and position of alighting for over 80% of the boarding transactions. The results are available at any desired time–space disaggregation. After some post-processing and after incorporating expansion factors to account for unobserved trips, we build public transport OD matrices. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

The school bus is a major transportation mode for students in Canada. Unexpected delay of a school bus may be a major source of inconvenience for students and their parents. Accordingly, the provision of timely and reliable information on the expected arrivals of school buses would be of great benefit to them. This study develops an expected time of arrival (ETA) model for school buses. The model predicts arrival time from the input of two categories: the last several days' historical data and the current day's operational conditions. An operational strategy is additionally incorporated into the model to reduce the risk that an overestimated arrival time can result in missing the bus. This study evaluates the model using data collected from real-world operations of school buses on which a global positioning system-based automatic vehicle location (AVL) system is installed. The proposed model consistently shows lower levels of prediction error than moving average and regression approaches. With the operational strategy, the model provides a sufficiently reliable service in which approximately 99%-100% of students do not miss the bus, with the tolerable wait time of 162-177. [ABSTRACT FROM AUTHOR]

Studies of municipal solid waste collection systems have traditionally relied upon information collected from time and motion studies or truck logs. This type of data collection has been expensive, the volume of data collected has been small, and the reliability of the data has been suspect. A recent project in Hamilton, Ontario, monitored five municipal solid waste collection vehicles using a global positioning system (GPS) as an alternative to traditional data collection methods. The study found that the GPS data are reliable, accurate, and suitable for a range of solid waste planning purposes. Data collection was automatic and relatively inexpensive. Analysis of the data identified significant differences in the performance of the vehicles on different routes. Data collection using GPS is an improvement over traditional data collection methods, but the large volume of data generated will provide challenges for waste managers. [ABSTRACT FROM AUTHOR]

Abstract: This study aims to justify a systematic decision process for constructing a desirable logistics tracking system, based on information integrity risk associated. It consists of four interrelated executions of analyzing risk, assessing risk, prioritizing mitigations, and selecting mitigations. Failure Mode Effect Analysis, risk triangle, and cost-effect indicators are organized to provide a system solution, calibrated using focus group meeting and group decision process. In the empirical study, Automatic Vehicle Location system was initiated to track the container movements in a Taiwanese marine port. But the justification recommends adding four effective risk measures to finalize the system. The justified logistics tracking system is concluded to be more effective than the current manual escort in smuggling prevention, i.e. information risk reduced by 62%. Besides, direct cost saving of the carriers is secured, together with the considerable macro benefits derived from fastened physical flow and improved trade security, to increase the project feasibility. [Copyright &y& Elsevier]

In this paper, archived Automatic Vehicle Location and Automatic Passenger Counter data are used to evaluate actual bus running time variation in relation to scheduled service for Tri-Met, the transit provider for the Portland, Oregon metropolitan area. Given observed variation in running times, scheduled recovery times are found to be generally (though not universally) excessive. This results in an under-investment of resources in revenue service relative to non-revenue service. Analysis of trip level data reveals that bus operators are an important source of running time variation after controlling for such factors as route design, time of day and direction of service, and passenger activity. [ABSTRACT FROM AUTHOR]

• Walking time distributions for transfers from bus to rail platform. • Estimations based on smart card data and automatic vehicle location data. • Distributions obtained for both passengers walking directly and passengers performing an activity. • The methodology requires no information on walking paths or distances. • Methodology is applicable at scale and results for application to 129 stations are presented. Transfers are a major contributor to travel time unreliability for journeys in public transport. Thus, connections between services in the public transport network must be reliable. To plan such reliable transfers from e.g. busses to trains, it is crucial to know the necessary walking times from stops to platforms. This paper presents an innovative approach for estimation of walking time distributions from bus stops to train platforms based on a matching of smart card data and automatic vehicle location data. The observed times from bus stop to rail platform turns out to have a large variance, due to two reasons: differences in passenger walking speeds, and passengers who are doing activities during the transfer. To account for these variations a hierarchical Bayesian mixture model is applied, where the time for passengers walking directly and passengers doing activities during the transfer follows separate distributions. The proposed methodology is applied to 129 stations in the Eastern part of Denmark, where the tap-in devices are located at the train platform. Results from two stations with different characteristics are presented in details along with justifications and analyses of model accuracy. The outcome of the model with distributions of the necessary walking times from bus stops to train platforms is important input for timetabling connections, and the data-driven methodology can easily be applied at scale. [ABSTRACT FROM AUTHOR]

In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values. [ABSTRACT FROM AUTHOR]