The traveler costs of unplanned transport network disruptions: An activity-based modeling approach

In this paper we introduce an activity-based modeling approach for evaluating the traveler costs of transport network disruptions. The model handles several important aspects of such events: increases in travel time may be very long in relation to the normal day-to-day fluctuations; the impact of delay may depend on the flexibility to reschedule activities; lack of information and uncertainty about travel conditions may lead to under- or over-adjustment of the daily schedule in response to the delay; delays on more than one trip may restrict the gain from rescheduling activities. We derive properties such as the value of time and schedule costs analytically. Numerical calculations show that the average cost per hour delay increases with the delay duration, so that every additional minute of delay comes with a higher cost. The cost varies depending on adjustment behavior (less adjustment, loosely speaking, giving higher cost) and scheduling flexibility (greater flexibility giving lower cost). The results indicate that existing evaluations of real network disruptions have underestimated the societal costs of the events.transport network disruption, delay cost, schedule adjustment, activity-based model, information

The development of a model for predicting passenger acceptance of short-haul air transportation systems

Meaningful criteria and methodology for assessing, particularly in the area of ride quality, the potential acceptability to the traveling public of present and future transportation systems were investigated. Ride quality was found to be one of the important variables affecting the decision of users of air transportation, and to be influenced by several environmental factors, especially motion, noise, pressure, temperature, and seating. Models were developed to quantify the relationship of subjective comfort to all of these parameters and then were exercised for a variety of situations. Passenger satisfaction was found to be strongly related to ride quality and was so modeled. A computer program was developed to assess the comfort and satisfaction levels of passengers on aircraft subjected to arbitrary flight profiles over arbitrary terrain. A model was deduced of the manner in which passengers integrate isolated segments of a flight to obtain an overall trip comfort rating. A method was established for assessing the influence of other links (e.g., access, terminal conditions) in the overall passenger trip

Link Travel Time Prediction Based on O-D Matrix and Neural Networks

Ühistranspordi kasutajad on tihtipeale huvitatud täpsest reisiajast seetõttu, et tõhusalt aega planeerida. Kuid ebaregulaarsete reisiaegade tõttu on seda üsna keeruline teha. Reisiaegade muutused võivad olla põhjustatud näiteks ilmastikuoludest, liiklusõnnetustest ja liiklusnõudlusest. Intelligentse transpordisüsteemi kaasamisega ühistranspordi süsteemi muutus hõlpsamaks bussireisi andmete kogumine, sealhulgas ka reisiaegade kogumine. Kogutud andmeid on võimalik kasutada tulevaste reiside prognoosimiseks, rakendades erinevaid teaduslikke meetodeid, näiteks Kalmani filtrit, masinõpet ja tehisnärvivõrke. Antud lõputöö eesmärgiks on luua tehisnärvivõrgu mudel, mis ennustab tiheda liiklusega teekonna reisiaega. Selleks kasutatakse algpunkt-sihtpunkt maatriksit, mis on koostatud sama teekonna kohta kogutud GPS informatsioonist. Ennustustäpsuse arvutamiseks kasutati antud lõputöös ruutkeskmist viga (RMSE). Tulemuste analüüs näitas, et antud mudel on piisav tegemaks tulevaste reisiaegade ennustusi.In public transportation system, commuters are often interested in getting accurate travel time information regarding trips in the future in order to plan their future schedules effectively. However, this information is often difficult to predict due to the irregularities in travel time which are caused by factors like future weather conditions, road accidents and fluctuations in traffic demand. With the introduction of Intelligent Transportation System into public transport system, it has been easy to collect data regarding bus trips such as travel times data. The data collected can be used to make predictions regarding trips in the future by applying scientific methods like Kalman filter, machine learning, and deep learning neural network. The goal of this thesis is to develop a neural network model for predicting travel time information of a busy route using Origin-Destination matrix derived from a historical GPS dataset of the same route. The prediction accuracy of the NN model developed in this thesis was measured using Root Mean Square Error (RMSE). Analysis of the result showed that the model is sufficient for making predictions of travel time for trips in the future

Algorithmic Analysis of Intermodal Transport Network

Tato práce je zaměřena na analýzu intermodální dopravní sítě pomocí multikriteriálního algoritmu s ohledem na priority města. Nejprve popisujeme reprezentaci intermodální dopravní sítě. Poté definujeme úlohu analýzy nad danou reprezentací. Jedná se o algoritmickou analýzu, tedy na základě zadané poptávky cestujících vyhodnocujeme klíčové indikátory. Mezi zahrnuté indikátory patří počet přeplněných úseků spojů, doba jízdy všech cestujících a celkové náklady všech cestujících. Cílem analýzy je optimalizovat počet přeplněných úseků dopravní sítě tím, že nabídneme cestujícím alternativní jízdy. Tyto cesty se snaží vyhnout úsekům dopravní sítě, kde jsou spoje přeplněné. Vyhnout se lze vybráním jiného spoje veřejné dopravy, jízdou na kole, nebo využitím taxi služby. Popisujeme multikriteriální algoritmus, který pro každého cestujícího vyhledá vhodnou cestu, přičemž optimalizuje čtyři kritéria: obsazenost vozu, dobu jízdy, cestovní náklady a počet přestupů. Také implementujeme nástroj pro analýzu, který obsahuje tento multikriteriální algoritmus a z nalezených cest vypočítá chtěné klíčové indikátory. Pomocí našeho nástroje provádíme analýzu intermodální dopravní sítě hlavního města Prahy. Při evaluaci námi vygenerované poptávky cestujících dosahujeme snížení počtu přeplněných úseků spojů v intermodální dopravní síti o 79,4 %.This work focuses on the analysis of the intermodal transport network using a multi-criteria algorithm that considers preferences of the city. To perform the analysis, we first describe the representation of the intermodal transport network. Given the representation, we define the intermodal transport network analysis problem with preferences of the city. We aim at algorithmic analysis, which computes key performance indicators using given travel demand. Thus, we provide various key performance indicators, e.g., the number of overcrowded trip segments, the total duration of all passenger journeys, and the total costs of passenger journeys. The goal of the analysis is to optimize the number of overcrowded parts of the public transport network. To achieve the goal, we offer passengers alternative journeys. These journeys try to avoid public transport vehicles with occupancy beyond a certain level of comfort. In other words, a passenger may choose another public transport connection, ride a bike, or use a taxi service. We propose a multi-criteria algorithm that finds a suitable journey for each passenger while optimizing four criteria, i.e., vehicle occupancy, duration, costs, and the number of interchanges. We also implement an analysis tool that includes the multi-criteria algorithm and calculates the required key performance indicators. By using the analysis tool, we perform an analysis using the intermodal transport network of the capital city of Prague. In the evaluation, we achieve the reduction in the number of overcrowded trip segments in the intermodal transport network by 79.4 % on randomly generated travel demand

Development and application of dynamic models for predicting transit arrival times

Stochastic variations in traffic conditions and ridership often have a negative impact in transit operations resulting in the deterioration of schedule/headway adherence and lengthening of passenger wait times. Providing accurate information on transit vehicle arrival times is critical to reduce the negative impacts on transit users. In this study, models for dynamically predicting transit arrival times in urban settings are developed, including a basic model, a Kalman filtering model, link-based and stop-based artificial neural networks (ANNs) and Neural/Dynamic (ND) models. The reliability of these models is assessed by enhancing the microscopic simulation program CORSIM which can calculate bus dwell and passenger wait times based on time-dependent passenger demands and vehicle inter-departure times (headways) at stops. The proposed prediction models are integrated with the enhanced CORSIM individually to predict bus arrival times while simulating the operations of a bus transit route in New Jersey. The reliability analysis of prediction results demonstrates that ANNs are superior to the basic and Kalman filtering models. The stop-based ANN generally predicts more accurately than the link-based ANN. By integrating an ANN (either link-based or stop-based) with the Kalman filtering algorithm, two ND models (NDL and NDS) are developed to decrease prediction error. The results show that the performance of the ND models is fairly close. The NDS model performs better than the NDL model when stop-spacing is relatively long and the number of intersections between a pair of stops is relatively large. In the study, an application of the proposed prediction models to a real-time headway control model is also explored and experimented through simulating a high frequency light rail transit route. The results show that with the accurate prediction of vehicle arrival information from the proposed models, the regularity of headways between any pair of consecutive operating vehicles is improved, while the average passenger wait times at stops are reduced significantly

Scalable system for smart urban transport management

Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city

Are road transportation investments in line with demand projections? A gravity-based analysis for Turkey

This is the post-print version of the article which has been published and is available at the link below.In this research, an integrated gravity-based model was built, and a scenario analysis was conducted to project the demand levels for routes related to the highway projects suggested in TINA-Turkey. The gravity-based model was used to perform a disaggregated analysis to estimate the demand levels that will occur on the routes which are planned to be improved in specific regions of Turkey from now until 2020. During the scenario development phase for these gravity-based models, the growth rate of Turkey's GDP, as estimated by the World Bank from now until 2017, was used as the baseline scenario. Besides, it is assumed that the gross value added (GVA) of the origin and destination regions of the selected routes will show a pattern similar to GDP growth rates. Based on the estimated GDP values, and the projected GVA growth rates, the demand for each selected route was projected and found that the demand level for some of these road projects is expected to be very low, and hence additional measures would be needed to make these investments worthwhile

Single-machine scheduling with stepwise tardiness costs and release times

We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity results that characterize some cases of the problem as pseudo-polynomially solvable. For the difficult-to-solve cases of the problem, we develop mathematical programming formulations, and propose heuristic algorithms. We test the formulations and heuristic algorithms on randomly generated single-machine scheduling problems and real-life datasets for the hump sequencing problem. Our experiments show promising results for both sets of problems

Road traffic open data in Sweden: Availability and commercial exploitation - A research study on the state of open transportation data in Sweden

This chapter includes a description of how the study was conducted. In order to explore the possibilities for private companies to use open data, an extensive literature review was conducted. Furthermore, this helped to get familiarized with the subject of open data and understand how it is utilized today by public companies. While researching different methods for data analytics that are being used in transportation, it was found that predictive analytics was one of the most prominent methods as it can be used in numerous ways in order to improve predictions and planning within organizations. The use of predictive analytics in transportation includes predicting delays and traffic conditions which were found to be appropriate areas of analytics with regards to the types of open data that are commonly available. Hence, these will be the areas of transport analytics that will be focused on in this study. In order to analyze the full potential of open transport data, both as a means of improving existing businesses as well as to allow for new business opportunities to originate, the methodology had to be considered accordingly. To scope out opportunities for improvement of business activities, research projects were reviewed where a number of types of open transport-related data were used to predict future outcomes of traffic conditions and events in public transportation that could have potential impacts on how daily activities within transportation organizations are performed. The projects were chosen based on the potential accessibility that the data used for the analysis has in Swedish open data sources, in order to make sure that corresponding solutions to the problems are feasible to perform in Sweden. Furthermore, in order to analyze the potential for new businesses to arise from available open data, several existing companies that have gained their success through the use of such data were studied to gain an insight into how value can be extracted from it. To analyze the accessibility of relevant open data in Sweden, Trafiklab, and Trafikverket, two open data sources for transportation-related data have been used. These were chosen in a screening method of the biggest open data sources that offer a large amount of data publicly in Sweden.Incomin