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Short-term prediction of outbound truck traffic from the exchange of information in logistics hubs: A case study for the port of Rotterdam

Ali Nadi, Salil Sharma, Maaike Snelder, Taoufik Bakri, Hans van Lint, Lóránt Tavasszy
2021 Transportation Research Part C: Emerging Technologies  
We formulate 2 scenarios to evaluate the forecasting abilities of the model. The model predicts lag and nonproportional responses of truck flows to changes in container turnover at terminals.  ...  Short-term traffic prediction is an important component of traffic management systems. Around logistics hubs such as seaports, truck flows can have a major impact on the surrounding motorways.  ...  The authors would like to thank Portbase and NDW for providing us with port community system data and traffic data, respectively. Appendix A.  ... 
doi:10.1016/j.trc.2021.103111 fatcat:yrbjlt3vrndmhikb3i4vz2o6re

Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

Billy M. Williams, Lester A. Hoel
2003 Journal of transportation engineering  
This foundation rests on the Wold decomposition theorem and on the assertion that a one-week lagged first seasonal difference applied to discrete interval traffic condition data will yield a weakly stationary  ...  This article presents the theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes.  ...  Department of Transportation, and the Federal Highway Administration through the Mid-Atlantic Universities Transportation Center.  ... 
doi:10.1061/(asce)0733-947x(2003)129:6(664) fatcat:o6h6maj5tnbjdkmwi7chwn2eti

Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints

Milan Simunek, Zdenek Smutny
2020 Applied Sciences  
The designed model comprises three submodels and combines parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information.  ...  Traffic speed prediction for a selected road segment from a short-term and long-term perspective is among the fundamental issues of intelligent transportation systems (ITS).  ...  Daily Aggregation In certain cases, it was suitable to consider even a higher level of aggregation-by days.  ... 
doi:10.3390/app11010315 fatcat:ajzndas47ndktebjp5wyrqvsdu

Potentialities of Autonomous Vehicles for Online Monitoring of Motorway Traffic Volume

Hyun-ho Chang, Byoung-jo Yoon
2018 Journal of Advanced Transportation  
The results show that online motorway traffic volume can be effectively monitored throughout the day with 5.69% average error at the 14.91% penetration rate of AVs during the daytime.  ...  To demonstrate this opportunity, this paper proposes a new method to monitor real-time motorway traffic volumes for road locations where no detector is installed using AV traffic volume.  ...  Acknowledgments This work was supported by the University of Incheon (International Cooperative) Research Grant in 2014.  ... 
doi:10.1155/2018/4276593 fatcat:xaag3gciyrdkhfvawehdwkshve

An Innovative Integration Methodology of Independent Data Sources to Improve the Quality of Freight Transport Surveys

Oliver Roider, Gerd Sammer, Christoph Link, Rudolf Bauer, Werner Schachinger
2015 Transportation Research Procedia  
The methodology was applied to data from the year 2009. Results show the reliability and plausibility of the methodology, indicated by a high correlation with high quality roadside traffic counts.  ...  The methodology comprises four steps, using data of the Austrian and European freight transport statistics, data of roadside interviews of truck drivers, and data of counting stations and toll gantries  ...  funding framework program IV2Splus and the Motorway and Expressway Financing Limited Company of Austria (Asfinag).  ... 
doi:10.1016/j.trpro.2015.12.043 fatcat:ipz5x2atbjcuxboanngji5bzwu

Travel Speed Prediction with a Hierarchical Convolutional Neural Network and Long Short-Term Memory Model Framework [article]

Wei Wang Shenzhen Urban Transport Planning Center Co. Ltd, China)
2018 arXiv   pre-print
To capture traffic seasonal variations, time of the day and day of the week indicators are fused with trained features.  ...  A deep CNN model is employed to learn the spatio-temporal traffic patterns of the input graphs, which are then fed into a deep LSTM model for sequence learning.  ...  travel time at flow V, t0 is the free flow travel time, n is the power of the flow delay curve, A is a derived parameter based on the user defined free flow speed S0 and speed at the link capacity S2.  ... 
arXiv:1809.01887v2 fatcat:bz2cgzp5dfcfpoenhyjasqevvu

Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK)

Jian Zhong, Christina Hood, Kate Johnson, Jenny Stocker, Jonathan Handley, Mark Wolstencroft, Andrea Mazzeo, Xiaoming Cai, William James Bloss
2021 Atmosphere  
For this air quality modelling application of task farming, the optimisation process has reduced weeks of model execution time to approximately 35 h for a single model configuration of annual calculations  ...  The model represents the full range of source types (point, road and grid sources) occurring in an urban area at high resolution.  ...  The authors also thank Transport for West Midlands (TfWM) and Birmingham City Council for provision of traffic data, previous modelling and reports.  ... 
doi:10.3390/atmos12080983 fatcat:y2gd7z47rjej5kkjxu5qhv5jhi

Managed Lane as Strategy for Traffic Flow and Safety: A Case Study of Catania Ring Road

Salvatore Cafiso, Alessandro Di Graziano, Tullio Giuffrè, Giuseppina Pappalardo, Alessandro Severino
2022 Sustainability  
Therefore, the present and further increase of traffic flow is affecting the operational and safety performance of several roadway categories.  ...  Despite the expected improvements in traffic capacity, the HSR poses safety issues particularly in specific locations (e.g., interchanges) and for the operation of the transition phase for opening and  ...  providing a series of indications such as average speeds, expected hourly and daily traffic flows, or service levels.  ... 
doi:10.3390/su14052915 doaj:a9469a86fda84b958ddec0f1f0b7015e fatcat:xo2t5czjlfhf3mhghbuwmo4pqu

The Impact of the COVID-19 Movement Restrictions on the Road Traffic in the Czech Republic during the State of Emergency

Milan Simunek, Zdenek Smutny, Michal Dolezel
2021 Journal of Advanced Transportation  
For the prediction of the prepandemic traffic conditions and their comparison with the measured values in the period of the state of emergency, a long-term traffic speed prediction ensemble model consisting  ...  The exception was motorways, where a different trend in traffic was found.  ...  from an institutional fund (IP400040) for Long-Term Conceptual Development of Science and Research and an internal grant (F4/23/2019) at the Faculty of Informatics and Statistics of the Prague University  ... 
doi:10.1155/2021/6622028 doaj:c63e314026d2488199bb2abe1ddd4ea1 fatcat:4fxjsccoi5c3pn4mv5c6ds3yke

Estimating Historical Hourly Traffic Volumes via Machine Learning and Vehicle Probe Data: A Maryland Case Study [article]

Przemysław Sekuła, Nikola Marković, Zachary Vander Laan, Kaveh Farokhi Sadabadi
2018 arXiv   pre-print
For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/hr, which provides  ...  To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes  ...  The authors are grateful to the I-95 Corridor Coalition for funding this work through the Volume and Turning Movement Project.  ... 
arXiv:1711.00721v2 fatcat:y3r6gpscpnb6lpt4s6dagfuuue

Random forest meteorological normalisation models for Swiss PM10 trend analysis

Stuart K. Grange, David C. Carslaw, Alastair C. Lewis, Eirini Boleti, Christoph Hueglin
2018 Atmospheric Chemistry and Physics  
</strong> Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series.  ...  height, and time variables to explain daily PM<sub>10</sub> concentrations.  ...  The authors thank the University of York database administrators of Carl Stovell and his team and Rudolf Weber from the Swiss Federal Office for the Environment for the delivery of the Härkingen-A1 and  ... 
doi:10.5194/acp-18-6223-2018 fatcat:w4yimrqeo5df7gfnalj7qzwlgu

Low-Dimensional Model for Bike-Sharing Demand Forecasting that Explicitly Accounts for Weather Data

Guido Cantelmo, Rafał Kucharski, Constantinos Antoniou
2020 Transportation Research Record  
In this paper, we predict the demand pattern of New York City bikes with a low-dimensional approach utilizing three-level data clustering.  ...  In this paper, we synthesize more than 17 million trips into daily and zonal vectors of movement, which combined with weather data allow forecasting of the trip demand.  ...  Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research has been partially sponsored by the European Union's  ... 
doi:10.1177/0361198120932160 fatcat:3t7njxovvrewpcp6fsluv6bwia

Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

Aniekan Essien, Ilias Petrounias, Pedro Sampaio, Sandra Sampaio
2019 2019 IEEE International Conference on Big Data and Smart Computing (BigComp)  
The impact of these factors can affect traffic flow parameters by influencing travel time, density, and operating speed.  ...  Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks.  ...  Fig. 2 : 2 Study Area Fig. 4 : 4 LSTM time expanded structure Fig. 3: Data Fusion Approach for Urban Traffic Speed Prediction weather data aggregation level.  ... 
doi:10.1109/bigcomp.2019.8679231 dblp:conf/bigcomp/EssienPSS19 fatcat:utnc26mqmbar3otpjlf2rijmv4

A network traffic flow model for motorway and urban highways

Chao Yang, Rasa Remenyte-Prescott, John Andrews
2014 Journal of the Operational Research Society  
It forecasts the traffic flow rates, queue propagation at the junctions and travel delays through the network.  ...  The research reported in this paper develops a network level traffic flow model (NTFM) which is applicable for both motorway and urban roads.  ...  One novel feature of the model is that both motorway and urban networks are evaluated based on the same principle of considering a maximum capacity flow rate at the junctions where flows compete, balancing  ... 
doi:10.1057/jors.2013.86 fatcat:bkydkosx7veh3eckml37ulekdi

Short-term real-time traffic prediction methods: A survey

Joaquim Barros, Miguel Araujo, Rosaldo J. F. Rossetti
2015 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)  
This research leads to an understanding of the many advantages, disadvantages and trade-offs of the approaches studied and provides useful insights for future development.  ...  Despite the predominance of model-driven solutions for the last years, data-driven approaches also present good results suitable for Traffic Management usage.  ...  Taking into account the repeatable pattern of traffic flow, they built three time series: s 1 (daily), s 2 (weekly) and s 3 (hourly).  ... 
doi:10.1109/mtits.2015.7223248 dblp:conf/mtits/BarrosAR15 fatcat:urupgqhs4bddfcmte5objwsvq4
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