TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA

ABSTRACT: The prediction of accurate traffic information such as speed, travel time, and congestion state is a very important task in many Intelligent Transportations Systems (ITS) applications. However, the dynamic changes in traffic conditions make this task harder. In fact, the type of road, such as the freeways and the highways in urban regions, can influence the driving speeds and the congestion state of the corresponding road. In this paper, we present a NNs-based model to predict the congestion state in roads. Our model handles new inputs and distinguishes the dynamic traffic patterns in two different types of roads: highways and freeways. The model has been tested using a big GPS database gathered from vehicles circulating in Tunisia. The NNs-based model has shown their capabilities of detecting the nonlinearity of dynamic changes and different patterns of roads compared to other nonparametric techniques from the literature. ABSTRAK: Ramalan maklumat trafik yang tepat seperti kelajuan, masa perjalanan dan keadaan kesesakan adalah tugas yang sangat penting dalam banyak aplikasi Sistem Pengangkutan Pintar (ITS). Walau bagaimanapun, perubahan keadaan lalu lintas yang dinamik menjadikan tugas ini menjadi lebih sukar. Malah, jenis jalan raya, seperti jalan raya dan lebuh raya di kawasan bandar, boleh mempengaruhi kelajuan memandu dan keadaan kesesakan jalan yang sama. Dalam makalah ini, kami membentangkan model berasaskan NN untuk meramalkan keadaan kesesakan di jalan raya. Model kami mengendalikan input baru dan membezakan corak trafik dinamik dalam dua jenis jalan raya yang lebuh raya dan jalan raya. Model ini telah diuji menggunakan pangkalan data GPS yang besar yang dikumpulkan dari kenderaan yang beredar di Tunisia. Model berasaskan NNs telah menunjukkan keupayaan mereka untuk mengesan ketiadaan perubahan dinamik dan pola jalan yang berbeza berbanding dengan teknik nonparametrik yang lain dari kesusasteraan

Vehicle Travel Time Estimation Using Sequence Prediction

This paper proposes a region-based travel time and traffic speed prediction method using sequence prediction. Floating Car Data collected from 8,317 vehicles during 34 days are used for evaluation purposes. Twelve districts are chosen and the spatio-temporal non-linear relations are learned with Recurrent Neural Networks. Time estimation of the total trip is solved by travel time estimation of the divided sub-trips, which are constituted between two consecutive GNSS measurement data. The travel time and final speed of sub-trips are learned with Long Short-term Memory cells using sequence prediction. A sequence is defined by including the day of the week meta-information, dynamic information about vehicle route start and end positions, and average travel speed of the road segment that has been traversed by the vehicle. The final travel time is estimated for this sequence. The sequence-based prediction shows promising results, outperforms function mapping and non-parametric linear velocity change based methods in terms of root-mean-square error and mean absolute error metrics.</p

Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach

Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly impact traffic conditions. This paper proposes a multi-dimensional spatio-temporal graph attention-based traffic prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane closure events, temperature, and visibility across the transportation network. The approach is based on a graph attention network architecture, which also learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic speed and lane closure data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). The corresponding weather data were downloaded from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). For comparison, the numerical experiments implement three alternative models which do not allow for the multi-dimensional input. The M-STGAT is shown to outperform the three alternative models, when performing tests using our primary data set for prediction with a 30-, 45-, and 60-minute prediction horizon, in terms of three error measures: Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). However, the model's transferability can vary for different transfer data sets and this aspect may require further investigation.Comment: Transportation Research Board Annual Meeting 202

Traffic Volume Forecasting Model of Freeway Toll Stations During Holidays – An SVM Model

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current and previous 7-day traffic volumes are used as the dependent and independent variables for model learning, respectively. This paper found that the basic SVM model is not accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model

A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed

Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented. Document type: Articl

Optimization of Coastal Cruise Lines in China

The paper analyzes the current state of the Chinese cruise market and presents the idea of building a business model of coastal cruising. The cruise demand of middle-income families, which includes the desired travel days, ports of call, is surveyed. The data of the previous non-cruise travels and the data of future cruises of middle-income families are used to develop a model designed to identify the maximum passenger volume with minimum operating costs while taking cruise itineraries and schedules into account. A matrix coding genetic algorithm was designed to solve the model. The case study found that a voyage of 4.79 days results in equilibrium, that the annual demand is 200,840 passengers, and that the daily voyage cost is 0.843 million Yuan

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency

Algoritmos de aprendizado de máquina supervisionados aplicados em transportes : comparativo com Modelo Logit Multinomial para escolha modal

Como o planejamento no transporte urbano desempenha um papel essencial para o desenvolvimento sustentável dos sistemas de transporte, torna-se evidente a necessidade de explorar novas técnicas de análise para aprimorar a eficiência e a eficácia. Em particular, o uso de técnicas de Machine Learning (Aprendizado de Máquina) tem se mostrado promissor para lidar com os desafios complexos relacionados ao planejamento do transporte urbano. A incorporação desses algoritmos pode melhorar a capacidade de análise de dados e fornecer diretrizes para a tomada de decisões. Dado esse contexto, a presente dissertação foi dividida em dois artigos que tem por objetivos: (i) desenvolvimento de uma revisão sistemática da literatura para analisar de forma quantitativa os estudos existentes sobre planejamento de transporte urbano com modelos de Machine Learning, identificar os principais temas abordados, quais são as aplicações e como podem auxiliar na otimização dos sistemas de transporte urbano (ii) comparar modelos tradicionais de escolha discreta com algoritmos de Aprendizado de Máquina, a fim de analisar a previsão da escolha modal, utilizando dados provenientes de uma pesquisa de Preferência Declarada (PD) realizada em Porto Alegre em 2019. Os resultados obtidos na revisão sistemática indicam que os métodos de Aprendizado de Máquina estão em crescente utilização no planejamento de transportes. Dentre os métodos analisados, os modelos de previsão de demanda de tráfego e de transporte público se destacaram como os mais empregados na literatura. Além desses, outros métodos, como reconhecimento de sinais de trânsito, detecção de semáforos, classificação de veículos, detecção de pedestres, planejamento de tempo de viagem e de itinerário e comparativos entre algoritmos diferentes também foram frequentemente utilizados. Os resultados do estudo comparativo indicam que o modelo de Logit Multinomial (MLM) apresentou uma acurácia preditiva significativamente maior em comparação com os outros modelos de Aprendizado de Máquina testados. A taxa de acerto do MLM foi de 52,03%, seguida pelo método de Floresta Aleatória (FA) com 41,79%, e as Redes Neurais Artificiais (RNAs) com 40,94%. Esses resultados podem ser explicados pelo fato de que a base de dados utilizada na análise continha poucas observações para os modos de transporte Lotação e Táxi.As urban transportation planning plays an essential role in the sustainable development of transportation systems, there is a clear need to explore new analysis techniques to improve the efficiency and effectiveness of planning. In particular, the use of Machine Learning (ML) techniques has shown promise in dealing with complex challenges related to urban transportation planning. The incorporation of these algorithms can significantly improve data analysis capabilities and provide guidelines for decision-making. Given this context, this dissertation is divided into two articles that aim to: (i) develop a systematic literature review to quantitatively analyze existing studies on urban transportation planning with Machine Learning models, identify the main themes addressed, what are the applications, and how they can assist in optimizing urban transportation systems, (ii) compare traditional discrete choice models with Machine Learning algorithms to analyze modal choice prediction using data from a Stated Preference (SP) survey conducted in Porto Alegre in 2019. The results of the systematic review indicate that Machine Learning methods are increasingly being used in transportation planning. Among the methods analyzed, traffic and public transport demand prediction models stood out as the most frequently used in the literature. Additionally, other methods such as traffic sign recognition, traffic signal detection, vehicle classification, pedestrian detection, travel time and itinerary planning, and comparative studies between different algorithms were also frequently used. The results of the comparative study indicate that the Multinomial Logit Model (MLM) presented significantly higher predictive accuracy compared to other Machine Learning models tested. The MLM accuracy rate was 52.03%, followed by the Random Forest (RF) method with 41.79%, and the Artificial Neural Networks (ANNs) with 40.94%. These results may be explained by the fact that the database used in the analysis contained few observations for the Lotação and Taxi transportation modes