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Author: Ahmed Talat M. Halawani Publisher: ISBN: Category : Buses Languages : en Pages : 92
Book Description
Letting buses operate in mixed traffic is the least costly way to accommodate transit, but that exposes transit to traffic congestion which causes delay and service unreliability. Understanding the real cost that traffic congestion imposes on both passengers and operating agencies is critical for the efficient and equitable management of road space. This study aims to develop a systematic methodology to estimate those costs using Automated Vehicle Location data. Traffic congestion increases cost to both transit operators and passengers. For transit operators, congestion results in longer running times and increased recovery time. To passengers, traffic congestion increases riding time and, because of how congestion increases unreliability, waiting time. Using data from a low-traffic period as a baseline, incremental running time in each period can be calculated. However, some of this incremental running time is due to the greater passenger volumes that typically accompany higher traffic periods. Passenger counts and a regression model for dwell time, estimated from detailed ride check data, are used to estimate the passenger volume effect on running time so that incremental delay due to congestion can be identified. Cost impacts for operators and passengers follow directly. Observed running time variability is a combination of variability due to greater demand, variability in the schedule, inherent variability in running time, variability due to imperfect operating control, and variability due to traffic congestion. Methods are developed to estimate the first four components so that incremental variability due to traffic congestion can be identified for each period, again using a low traffic period as a baseline. From this incremental variability, we can estimate the additional recovery time needed as well as increases in passenger waiting time and potential travel time, which the difference between budgeted travel time and actual travel time. The methodology was tested on nine different bus routes including both high and low frequency routes. Overall, the average impact on operating cost is $20.4 per vehicle-hour, and the average impact to passengers is $1.30 per passenger; naturally, these impacts are far greater during peak periods.
Author: Ahmed Talat M. Halawani Publisher: ISBN: Category : Buses Languages : en Pages : 92
Book Description
Letting buses operate in mixed traffic is the least costly way to accommodate transit, but that exposes transit to traffic congestion which causes delay and service unreliability. Understanding the real cost that traffic congestion imposes on both passengers and operating agencies is critical for the efficient and equitable management of road space. This study aims to develop a systematic methodology to estimate those costs using Automated Vehicle Location data. Traffic congestion increases cost to both transit operators and passengers. For transit operators, congestion results in longer running times and increased recovery time. To passengers, traffic congestion increases riding time and, because of how congestion increases unreliability, waiting time. Using data from a low-traffic period as a baseline, incremental running time in each period can be calculated. However, some of this incremental running time is due to the greater passenger volumes that typically accompany higher traffic periods. Passenger counts and a regression model for dwell time, estimated from detailed ride check data, are used to estimate the passenger volume effect on running time so that incremental delay due to congestion can be identified. Cost impacts for operators and passengers follow directly. Observed running time variability is a combination of variability due to greater demand, variability in the schedule, inherent variability in running time, variability due to imperfect operating control, and variability due to traffic congestion. Methods are developed to estimate the first four components so that incremental variability due to traffic congestion can be identified for each period, again using a low traffic period as a baseline. From this incremental variability, we can estimate the additional recovery time needed as well as increases in passenger waiting time and potential travel time, which the difference between budgeted travel time and actual travel time. The methodology was tested on nine different bus routes including both high and low frequency routes. Overall, the average impact on operating cost is $20.4 per vehicle-hour, and the average impact to passengers is $1.30 per passenger; naturally, these impacts are far greater during peak periods.
Author: Publisher: ISBN: Category : Bus travel Languages : en Pages : 31
Book Description
Congestion can influence transit service attractiveness, operating cost, and system efficiency. This paper examines archived transit data to compare the effects of different intersection geometries on traffic congestion. The Tri-County Metropolitan Transportation District of Oregon (TriMet) has been archiving automatic vehicle location (AVL) and automatic passenger count (APC) data for all bus trips at the stop level since 1997 as part of their bus dispatch system (BDS). In 2013, TriMet implemented a higher resolution bus AVL data collection system. This 5-second resolution (5-SR) bus position data provides information about buses between stops in addition to their stop level data. The 5-SR data allows for the creation of a quantitative congestion analysis at specific locations. The objective of this paper is to use the high-resolution congestion analysis for a particular bus route in Portland, OR to analyze intersections with similar demands (i.e. through bus travel, similar traffic volumes, and far-side bus-stops) but different geometries. Results suggest that buses moving through intersections with a separated right turn lane may experience significantly less congestion than buses moving through intersections with a combination through/right turn lane. Interestingly, the travel lane (i.e. in the right turn or through lane) of the buses also may make a significant difference to the congestion experience by buses at the intersection. Buses in the through lane may experience less delay than buses in the right-turn lane.
Author: Jorge Freire Sousa Publisher: Springer Science & Business Media ISBN: 3319046306 Category : Technology & Engineering Languages : en Pages : 474
Book Description
This volume brings together works resulting from research carried out by members of the EURO Working Group on Transportation (EWGT) and presented during meetings and workshops organized by the Group under the patronage of the Association of European Operational Research Societies in 2012 and 2013. The main targets of the EWGT include providing a forum to share research information and experience, encouraging joint research and the development of both theoretical methods and applications, and promoting cooperation among the many institutions and organizations which are leaders at national level in the field of transportation and logistics. The primary fields of interest concern operational research methods, mathematical models and computation algorithms, to solve and sustain solutions to problems mainly faced by public administrations, city authorities, public transport companies, service providers and logistic operators. Related areas of interest are: land use and transportation planning, traffic control and simulation models, traffic network equilibrium models, public transport planning and management, applications of combinatorial optimization, vehicle routing and scheduling, intelligent transport systems, logistics and freight transport, environment problems, transport safety, and impact evaluation methods. In this volume, attention focuses on the following topics of interest: · Decision-making and decision support · Energy and Environmental Impacts · Urban network design · Optimization and simulation · Traffic Modelling, Control and Network Traffic Management · Transportation Planning · Mobility, Accessibility and Travel Behavior · Vehicle Routing
Author: Peter Gregory Furth Publisher: Transportation Research Board ISBN: 9780309068611 Category : Computers Languages : en Pages : 76
Book Description
This synthesis reviews the state of the practice in how data are analyzed. It addresses methods used to analyze data and what computer systems are used to store and process data. It also covers accuracy issues, including measurement error, and other problems including error in estimates. This document from the Transportation Research Board addresses agency experience with different data collection systems, giving attention to management error, the need for sampling, and methods for screening, editing, and compensating for data imperfection. Sample reports from selected U.S. and Canadian transit agencies are reproduced in this synthesis.
Author: Fumitaka Kurauchi Publisher: CRC Press ISBN: 1315353334 Category : Political Science Languages : en Pages : 198
Book Description
Collecting fares through "smart cards" is becoming standard in most advanced public transport networks of major cities around the world. Travellers value their convenience and operators the reduced money handling fees. Electronic tickets also make it easier to integrate fare systems, to create complex time and space differentiated fare systems, and to provide incentives to specific target groups. A less-utilised benefit is the data collected through smart cards. Records, even if anonymous, provide for a much better understanding of passengers’ travel behaviour as current literature shows. This information can also be used for better service planning. Public Transport Planning with Smart Card Data handles three major topics: how passenger behaviour can be estimated using smart card data, how smart card data can be combined with other trip databases, and how the public transport service level can be better evaluated if smart card data is available. The book discusses theory as well as applications from cities around the world and will be of interest to researchers and practitioners alike who are interested in the state-of-the-art as well as future perspectives that smart card data will bring.
Author: David G. Gerstle Publisher: ISBN: Category : Languages : en Pages : 94
Book Description
The benefits of bus automatic vehicle location (AVL) data are well documented (see e.g., Furth et al. (2006)), ranging from passenger-facing applications that predict bus arrival times to service-provider-facing applications that monitor network performance and diagnose performance failures. However, most other researchers' analyses tend to use data that they acquired through negotiations with transit agencies, adding a variable cost of time both to the transit agencies and to researchers. Further, conventional wisdom is that simple vehicle location trajectories are not suitable for evaluating bus performance (Furth et al. 2006). In this research, I use data that are free and open to the public. This access enables researchers and the general public to explore bus position traces. The research objective of this Master's Thesis is to build a computational system that can robustly evaluate bus performance across a wide range of bus systems under the hypothesis that a comparative approach could be fruitful for both retrospective and real-time analysis. This research is possible because a large number of bus providers have made their bus position, or AVL, data openly available. This research thus demonstrates the value of open AVL data, brings understanding to the limits of AVL data, evaluates bus performance using open data, and presents novel techniques for understanding variations in bus travel time. Specifically, this thesis demonstrates research to make the system architecture robust and fruitful: " This thesis explores the exceptions in the various datasets to which the system must be robust. As academics and general public look to exploit these data, this research seeks to elucidate important considerations for and limitations of the data." Bus data are high-dimensional; this research strives to make them dually digestible and informative when drawing conclusions across a long timescale. Thus, this research both lays the foundation for a broader research program and finds more visually striking and fundamentally valuable statistics for understanding variability in bus travel times.
Author: Ran Hee Jeong Publisher: ISBN: Category : Languages : en Pages :
Book Description
Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.
Author: Jared Arthur Fijalkowski Publisher: ISBN: Category : Languages : en Pages : 131
Book Description
Most large transit agencies have shifted from collecting transit performance data manually to using automated data systems to measure performance. These systems, which include Automatic Vehicle Location (AVL), Automatic Passenger Counters (APC), and Automated Fare Collection (AFC), enable transit agencies to (1) collect larger and more detailed sets of performance data, (2) measure transit performance more from the perspective of customers, and (3) conduct more systematic service evaluation processes. While many transit agencies have adopted these data collection technologies, many have not modified their service planning processes to reflect the full advantages of automated data systems. This thesis evaluates the current performance metrics used by the Chicago Transit Authority (CTA) and the Massachusetts Bay Transportation Authority (MBTA) in their respective service planning processes. A set of recommended performance metrics are proposed in the categories of bus loading, service reliability, passenger demand, and cost effectiveness that take advantage of the benefits of automatically collected data. Next, a service planning process is proposed by which transit agencies can use automatically collected data to systematically evaluate bus transit performance at the route, corridor, and system levels. In addition to making general recommendations applicable to all large transit agencies, this thesis makes specific recommendations for the CTA and the MBTA to improve their respective service planning processes to make full use of the capabilities of automated data systems. This thesis finds that performance metrics which take advantage of the large and detailed data sets that automated data systems provide can more accurately and acutely identify performance problems, leading planners to develop better solutions. Also, the efficiencies in automated data collection compared with manual data collection allow transit agencies to perform a more comprehensive and systematic process by which the performance of all transit service is evaluated on a regular basis. Finally, while automated data systems provide a high level of detail about bus transit performance, contextual information about route operations remains critical to accurately identifying and resolving performance problems.
Author: Ahmed Talat M. Halawani
Author: Ahmed Talat M. Halawani
Author: 
Author: Zhong-Ren Peng
Author: Jorge Freire Sousa
Author: Peter Gregory Furth
Author: Fumitaka Kurauchi
Author: Beth Moore
Author: David G. Gerstle
Author: Ran Hee Jeong
Author: Jared Arthur Fijalkowski