Metode til beregning af køretider, trængsel og forsinkelser i kryds vha. GPS Data

  Kortlægning af trafikafviklingen på vejnettet har traditionelt været baseret på snittællinger og krydstællinger – udført ved både manuelle og automatiske tællinger. Metodemæssigt har det givet en meget detaljeret viden om trafikken i enkeltpunkter i vejnettet, men ingen præcis viden få meter væk fra målepunktet. Med logdata fra GPS modtagere i biler er situationen omvendt: En detaljeret viden om enkelte bilers adfærd på hele vejnettet, men til gengæld kun data fra en lille stikprøve af bilerne. I denne artikel præsenteres en metode til beregning af køretider, trængsels og forsinkelse på baggrund af GPS data fra biler – de såkaldte floating car data. Metoden er implementeret og afprøves på to signalregulerede kryds i Aalborg. Hovedkonklusionen er, at den afprøvede metode kan give en god indikation for hvor og hvornår, der er trængsel i et kryds

En billig GPS data analyse platform

Denne artikel præsenterer en komplet software platform til analyse af GPS data. Platformen er bygget udelukkende vha. open-source komponenter. De enkelte komponenter i platformen beskrives i detaljer. Fordele og ulemper ved at bruge open-source diskuteres herunder hvilke IT politiske tiltage, der er nødvendige i en organisation for at bruge open-source. Map-matching diskuteres i flere omgange fordi det har været en udfordring at få implementeret. Platformen er udrullet og taget i produktion hos et mindre selskab. Konklusionen er, at med et meget begrænset budget (15.000,- kr.) kan alle organisationer med et digitalt vejkort og GPS data begynde at lave trafikanalyser på disse data. Det er et krav, at der er passende IT kompetencer tilstede i organisationen

Metode til beregning af køretider, trængsel og forsinkelser i kryds vha. GPS Data

Kortlægning af trafikafviklingen på vejnettet har traditionelt været baseret på snittællinger og krydstællinger – udført ved både manuelle og automatiske tællinger. Metodemæssigt har det givet en meget detaljeret viden om trafikken i enkeltpunkter i vejnettet, men ingen præcis viden få meter væk fra målepunktet. Med logdata fra GPS modtagere i biler er situationen omvendt: En detaljeret viden om enkelte bilers adfærd på hele vejnettet, men til gengæld kun data fra en lille stikprøve af bilerne. I denne artikel præsenteres en metode til beregning af køretider, trængsels og forsinkelse på baggrund af GPS data fra biler – de såkaldte floating car data. Metoden er implementeret og afprøves på to signalregulerede kryds i Aalborg. Hovedkonklusionen er, at den afprøvede metode kan give en god indikation for hvor og hvornår, der er trængsel i et kryds

Processamento analítico de fluxos de dados de tráfego em tempo quase real

Mestrado em Sistemas de InformaçãoNos dias de hoje, as tecnologias com as quais temos contacto geram dados sobre a sua utilização e sobre o utilizador, com uma velocidade e variedade sem precedentes. Cria-se assim a necessidade de gerir os fluxos de dados e de transformar estes dados em informação armazenada de forma estruturada, inferindo sobre a mesma e retirando conclusões. As áreas de aplicação são diversas e uma das vertentes que tem recebido maior atenção é o processamento de dados referentes ao tráfego automóvel obtidos usando dispositivos GPS, que se devidamente tratados permitem dar informação adicional aos utilizadores sobre o estado do trânsito, encontrar os caminhos mais rápidos ou até fazer previsões sobre o tráfego no futuro. O objetivo desta dissertação consiste em implementar um protótipo que consiga fazer o processamento de um fluxo de dados obtidos em tempo real e estruturá-los de forma a dar respostas sobre o estado do tráfego no momento e no futuro próximo. Para conseguir dar estas respostas, serão considerados não só os dados recebidos em tempo real como também informação adquirida anteriormente, de forma a ser possível fazer comparações e tirar conclusões. O protótipo está dividido em três módulos principais: o pré-processamento e a análise de dados históricos; o processamento de dados de tráfego em tempo quase real; e a apresentação de resultados. O protótipo foi sujeito a testes e os seus resultados sujeitos a avaliação de forma a verificar a validade das respostas devolvidas ao utilizador.Nowadays, the technologies we handle generate data about their usage and the user, with an unprecedented rate and variety. This raises the need to manage all the data streams and to transform these data in information. This information is stored in a structured way allowing to infer about it and withdraw conclusions. There is a wide range of application areas, with the car traffic data processing receiving the most attention. These data are obtained from GPS devices and if properly processed, allow the user to have additional information about the traffic status, the faster way to a destination and even predictions on the future traffic status. This dissertation aims to implement a prototype able to process and structure the data streams in real-time, to ultimately present answers about the traffic status at the moment or even in a near future. These answers are obtained not only by the real-time information but also by previously acquired information. Having two sources of information allows to compare and withdraw statistical conclusions. The prototype is divided in three main modules: the pre-processing and analysis of historical data; the processing of traffic data in near real-time; and the results presentation. The prototype was subject to tests and their results subject to evaluation to verify the answers’ assertiveness

A Deep Learning Approach for Spatiotemporal-Data-Driven Traffic State Estimation

The past decade witnessed rapid developments in traffic data sensing technologies in the form of roadside detector hardware, vehicle on-board units, and pedestrian wearable devices. The growing magnitude and complexity of the available traffic data has fueled the demand for data-driven models that can handle large scale inputs. In the recent past, deep-learning-powered algorithms have become the state-of-the-art for various data-driven applications. In this research, three applications of deep learning algorithms for traffic state estimation were investigated. Firstly, network-wide traffic parameters estimation was explored. An attention-based multi-encoder-decoder (Att-MED) neural network architecture was proposed and trained to predict freeway traffic speed up to 60 minutes ahead. Att-MED was designed to encode multiple traffic input sequences: short-term, daily, and weekly cyclic behavior. The proposed network produced an average prediction accuracy of 97.5%, which was superior to the compared baseline models. In addition to improving the output performance, the model\u27s attention weights enhanced the model interpretability. This research additionally explored the utility of low-penetration connected probe-vehicle data for network-wide traffic parameters estimation and prediction on freeways. A novel sequence-to-sequence recurrent graph networks (Seq2Se2 GCN-LSTM) was designed. It was then trained to estimate and predict traffic volume and speed for a 60-minute future time horizon. The proposed methodology generated volume and speed predictions with an average accuracy of 90.5% and 96.6%, respectively, outperforming the investigated baseline models. The proposed method demonstrated robustness against perturbations caused by the probe vehicle fleet\u27s low penetration rate. Secondly, the application of deep learning for road weather detection using roadside CCTVs were investigated. A Vision Transformer (ViT) was trained for simultaneous rain and road surface condition classification. Next, a Spatial Self-Attention (SSA) network was designed to consume the individual detection results, interpret the spatial context, and modify the collective detection output accordingly. The sequential module improved the accuracy of the stand-alone Vision Transformer as measured by the F1-score, raising the total accuracy for both tasks to 96.71% and 98.07%, respectively. Thirdly, a real-time video-based traffic incident detection algorithm was developed to enhance the utilization of the existing roadside CCTV network. The methodology automatically identified the main road regions in video scenes and investigated static vehicles around those areas. The developed algorithm was evaluated using a dataset of roadside videos. The incidents were detected with 85.71% sensitivity and 11.10% false alarm rate with an average delay of 27.53 seconds. In general, the research proposed in this dissertation maximizes the utility of pre-existing traffic infrastructure and emerging probe traffic data. It additionally demonstrated deep learning algorithms\u27 capability of modeling complex spatiotemporal traffic data. This research illustrates that advances in the deep learning field continue to have a high applicability potential in the traffic state estimation domain

A Dynamic Navigation System with Distributed Mobile Sensors

本研究探討相較於當前使用靜態圖資計算導航路徑，或僅提供部分壅塞情報的導航系統，一個以分散式移動感應器來取得道路系統中使用者位置、速度等即時路況資訊的導航系統，是否能夠以真正即時的路況資訊為考量，推薦出避開壅塞、減少通勤時間，更符合使用者期望的導航路徑。由於沒有實際執行的軟硬體環境，本研究以系統模擬的方式進行實驗，比較使用兩種不同導航方式移動的系統在通勤時間方面是否有差異。實驗結果顯示，考量了全體移動物件在道路網路上的位置、速度而得出之壅塞資訊的新導航方式，比只考量靜態圖資、推薦路程最短的大路的一般導航更能省時、更有效率。而更進一步，本研究提出學習使用者的行為模式的模型，將使用者行為的個體差異納入導航路徑的考量，學習使用者的偏好，讓導航的產出更能滿足使用者的習慣使用者偏好學習的功能與效果。This thesis studies a new approach of real time navigation which acquires real time traffic flow information by treating every user in the road network as a sensor, and collecting the information like speed and location from every sensor. Then in this thesis, because of the lack of real world platform to run the test, a simulation experiment is established to see whether the assumption is correct. The result of the simulation experiment shows that with real time traffic congestion condition being known, the new navigation approach has recommended the better routing path for moving objects then basic navigation approach, with only distant and road classes being know. The last part of this thesis brings up a future vision with user-preference-learning mechanism which can provide better navigation recommendation to suit users’ behavior.論文摘要 IHESIS ABSTRACT II錄 III次 V一章　緒論 1一節　研究背景與動機 1二節　研究目的 3三節　章節介紹 4二章　文獻探討 5一節　導航理論起源 5二節　DYNAMIC TRAVEL TIME MAPS (DTTM) 6三節　DTTM ROUTING 演算法 6四節　研究模型起源 8三章　研究架構 12一節 研究架構 12二節 系統模型 15四章　模擬實驗系統設計 16一節 實驗方法 16二節 模擬系統 17三節 系統設計 23四節 模擬系統設定 28五章　實驗結果與分析 31一節 新導航方法之校準測試 31二節 新導航方法的效率展示 32三節 導航方法的路徑多樣性比較 39四節 實驗結果分析 40五節 權重調整之比較實驗 44六章　結論與展望 47一節 結論 47二節 限制 47三節 未來展望 48考文獻 51錄 5