Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

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

Multi-headed self-attention mechanism-based Transformer model for predicting bus travel times across multiple bus routes using heterogeneous datasets

Bus transit is a crucial component of transportation networks, especially in urban areas. Bus agencies must enhance the quality of their real-time bus travel information service to serve their passengers better and attract more travelers. Various models have recently been developed for estimating bus travel times to increase the quality of real-time information service. However, most are concentrated on smaller road networks due to their generally subpar performance in densely populated urban regions on a vast network and failure to produce good results with long-range dependencies. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database and the vehicle probe data. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. This study developed a multi-headed self-attention mechanism-based Univariate Transformer neural network to predict the mean vehicle travel times for different hours of the day for multiple stations across multiple routes. In addition, we developed Multivariate GRU and LSTM neural network models for our research to compare the prediction accuracy and comprehend the robustness of the Transformer model. To validate the Transformer Model's performance more in comparison to the GRU and LSTM models, we employed the Historical Average Model and XGBoost model as benchmark models. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. Only the historical average bus travel time was used as the input parameter for the Transformer model. Other features, including spatial and temporal information, volatility measures (e.g., the standard deviation and variance of travel time), dwell time, expected travel time, jam factors, hours of a day, etc., were captured from our dataset. These parameters were employed to develop the Multivariate GRU and LSTM models. The model's performance was evaluated based on a performance metric called Mean Absolute Percentage Error (MAPE). The results showed that the Transformer model outperformed other models for one-hour ahead prediction having minimum and mean MAPE values of 4.32 percent and 8.29 percent, respectively. We also investigated that the Transformer model performed the best during different traffic conditions (e.g., peak and off-peak hours). Furthermore, we also displayed the model computation time for the prediction; XGBoost was found to be the quickest, with a prediction time of 6.28 seconds, while the Transformer model had a prediction time of 7.42 seconds. The study's findings demonstrate that the Transformer model showed its applicability for real-time travel time prediction and guaranteed the high quality of the predictions produced by the model in the context of a complicated extensive transportation network in high-density urban areas and capturing long-range dependencies.Includes bibliographical references

Data Fusion for MaaS: Opportunities and Challenges

© 2018 IEEE. Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

[Abstract] This paper deals with the efficiency and sustainability of urban rail transit (URT) using exploratory data analytics (EDA) and data envelopment analysis (DEA). The first stage of the proposed methodology is EDA with already available indicators (e.g., the number of stations and passengers), and suggested indicators (e.g., weekly frequencies, link occupancy rates, and CO2 footprint per journey) to directly characterize the efficiency and sustainability of this transport mode. The second stage is to assess the efficiency of URT with two original models, based on a thorough selection of input and output variables, which is one of the key contributions of EDA to this methodology. The first model compares URT against other urban transport modes, applicable to route personalization, and the second scores the efficiency of URT lines. The main outcome of this paper is the proposed methodology, which has been experimentally validated using open data from the Transport for London (TfL) URT network and additional sources.Ministerio de Economía, Industria y Competitividad; TIN2016-75845-PAgencia Estatal de Investigación; SNEO-20161147Xunta de Galicia; ED431G2019/01Xunta de Galicia; ED431C 2017/04Xunta de Galicia; ED431G2019/0

Survey of ETA prediction methods in public transport networks

The majority of public transport vehicles are fitted with Automatic Vehicle Location (AVL) systems generating a continuous stream of data. The availability of this data has led to a substantial body of literature addressing the development of algorithms to predict Estimated Times of Arrival (ETA). Here research literature reporting the development of ETA prediction systems specific to busses is reviewed to give an overview of the state of the art. Generally, reviews in this area categorise publications according to the type of algorithm used, which does not allow an objective comparison. Therefore this survey will categorise the reviewed publications according to the input data used to develop the algorithm. The review highlighted inconsistencies in reporting standards of the literature. The inconsistencies were found in the varying measurements of accuracy preventing any comparison and the frequent omission of a benchmark algorithm. Furthermore, some publications were lacking in overall quality. Due to these highlighted issues, any objective comparison of prediction accuracies is impossible. The bus ETA research field therefore requires a universal set of standards to ensure the quality of reported algorithms. This could be achieved by using benchmark datasets or algorithms and ensuring the publication of any code developed

Heuristics and Biases in Travel Mode Choice

. This study applies experimental methods to analyze travel mode choice. Two different scenarios are considered. In the first scenario, subjects have to decide whether to commute by car or by metro. Metro costs are fixed, while car costs are uncertain and determined by the joint effect of casual events and traffic congestion. In the second scenario, subjects have to decide whether to travel by car or by bus, both modes in which costs are determined by the combination of chance and congestion. Subjects receive feedback information on the actual travel times of both modes. We find that individuals exhibit a marked preference for cars, are inclined to confirm their first choice and demonstrate travel mode stickiness. We conclude that travel mode choice is subject to heuristics and biases that lead to robust deviations from rational choice.travel mode choice, learning, information, heuristics, cognitive biases.