Прогнозирование пробок на улицах по известным данным о скорости автомобилей

В рамках проводимого компанией «Яндекс» конкурса «Интернет-математика 2010» была предложена задача прогноза скорости движения автомобилей в Москве на основе имеющихся данных за один месяц. Для оценки качества предсказания использовалась определенным образом составленная невязка между известными и предсказанными данными, а победитель определялся как получивший минимальное значение невязки. В настоящей работе описывается алгоритм, который позволил сделать прогноз скоростей наиболее точно

Short-Term Travel Time Prediction on Freeways

Short-term travel time prediction supports the implementation of proactive traffic management and control strategies to alleviate if not prevent congestion and enable rational route choices and traffic mode selections to enhance travel mobility and safety. Over the last decade, Bluetooth technology has been increasingly used in collecting travel time data due to the technology’s advantages over conventional detection techniques in terms of direct travel time measurement, anonymous detection, and cost-effectiveness. However, similar to many other Automatic Vehicle Identification (AVI) technologies, Bluetooth technology has some limitations in measuring travel time information including 1) Bluetooth technology cannot associate travel time measurements with different traffic streams or facilities, therefore, the facility-specific travel time information is not directly available from Bluetooth measurements; 2) Bluetooth travel time measurements are influenced by measurement lag, because the travel time associated with vehicles that have not reached the downstream Bluetooth detector location cannot be taken at the instant of analysis. Freeway sections may include multiple distinct traffic stream (i.e., facilities) moving in the same direction of travel under a number of scenarios including: (1) a freeway section that contain both a High Occupancy Vehicle (HOV) or High Occupancy Toll (HOT) lane and several general purpose lanes (GPL); (2) a freeway section with a nearby parallel service roadway; (3) a freeway section in which there exist physically separated lanes (e.g. express versus collector lanes); or (4) a freeway section in which a fraction of the lanes are used by vehicles to access an off ramp. In this research, two different methods were proposed in estimating facility-specific travel times from Bluetooth measurements. Method 1 applies the Anderson-Darling test in matching the distribution of real-time Bluetooth travel time measurements with reference measurements. Method 2 first clusters the travel time measurements using the K-means algorithm, and then associates the clusters with facilities using traffic flow model. The performances of these two proposed methods have been evaluated against a Benchmark method using simulation data. A sensitivity analysis was also performed to understand the impacts of traffic conditions on the performance of different models. Based on the results, Method 2 is recommended when the physical barriers or law enforcement prevent drivers from freely switching between the underlying facilities; however, when the roadway functions as a self-correcting system allowing vehicles to freely switching between underlying facilities, the Benchmark method, which assumes one facility always operating faster than the other facility, is recommended for application. The Bluetooth travel time measurement lag leads to delayed detection of traffic condition variations and travel time changes, especially during congestion and transition periods or when consecutive Bluetooth detectors are placed far apart. In order to alleviate the travel time measurement lag, this research proposed to use non-lagged Bluetooth measurements (e.g., the number of repetitive detections for each vehicle and the time a vehicle spent in the detection zone) for inferring traffic stream states in the vicinity of the Bluetooth detectors. Two model structures including the analytical model and the statistical model have been proposed to estimate the traffic conditions based on non-lagged Bluetooth measurements. The results showed that the proposed RUSBoost classification tree achieved over 94% overall accuracy in predicting traffic conditions as congested or uncongested. When modeling traffic conditions as three traffic states (i.e., the free-flow state, the transition state, and the congested state) using the RUSBoost classification tree, the overall accuracy was 67.2%; however, the accuracy in predicting the congested traffic state was improved from 84.7% of the two state model to 87.7%. Because traffic state information enables the travel time prediction model to more timely detect the changes in traffic conditions, both the two-state model and the three-state model have been evaluated in developing travel time prediction models in this research. The Random Forest model was the main algorithm adopted in training travel time prediction models using both travel time measurements and inferred traffic states. Using historical Bluetooth data as inputs, the model results proved that the inclusion of traffic states information consistently lead to better travel time prediction results in terms of lower root mean square errors (improved by over 11%), lower 90th percentile absolute relative error ARE (improved by over 12%), and lower standard deviations of ARE (improved by over 15%) compared to other model structures without traffic states as inputs. In addition, the impact of traffic state inclusion on travel time prediction accuracy as a function of Bluetooth detector spacing was also examined using simulation data. The results showed that the segment length of 4~8 km is optimal in terms of the improvement from using traffic state information in travel time prediction models

Real Time Prediction of Traffic Speed and Travel Time Characteristics on Freeways

Travel time is one of the important transportation performance measures, and represents the quality of service for most of the facilities. In other words, one of the essential goals of any traffic treatment is to reduce the average travel time. Therefore, extensive work has been done to measure, estimate, and predict travel time. Using historical observations, traditional traffic analysis methods try to calibrate empirical models to estimate the average travel time of different transportation facilities. However, real-time traffic responsive management strategies require that estimates of travel time also be available in real-time. As a result, real time estimation and prediction of travel time has attracted increasing attention. Various factors influence the travel time of a road segment including: road geometry, traffic demand, traffic control devices, weather conditions, driving behaviors, and incidents. Consequently, the travel time of a road segment varies as a result of the variation of the influencing factors. Predicting near-future freeway traffic conditions is challenging, especially when traffic conditions are in transition from one state to another (e.g. changing from free flow conditions to congestion and vice versa). This research aims to develop a method to perform real-time prediction of near-future freeway traffic stream characteristics (namely speed) and that relies on spot speed, volume, and occupancy measurements commonly available from loop detectors or other similar traffic sensors. The framework of this study consists of a set of individual modules. The first module is called the Base Predictor. This module provides prediction for traffic variables while state of the traffic remains constant i.e free flow or congested. The Congestion Detection Module monitors the traffic state at each detector station of the study route to identify whether traffic conditions are congested or uncongested. When a congestion condition is detected, the Traffic Propagation Module is activated to update the prediction results of the Steady-State Module. The Traffic Propagation Module consists of two separate components: Congestion Spillback activates when traffic enters a congested state; Congestion Dissipation is activated when a congested state enters a recovery phase. The proposed framework of this study is calibrated and evaluated using data from an urban expressway in the City of Toronto, Canada. Data were obtained from the westbound direction of the Gardiner Expressway which has a fixed posted speed limit of 90 km/hr. This expressway is equipped with mainline dual loop detector stations. Traffic volume, occupancy and speed are collected every 20 seconds for each lane at all the stations. The data set used in this study was collected over the period from January 2009 to December 2011. For the Steady-State Module, a model based on Kalman filter was developed to predict the near future traffic conditions (speed, flow, and occupancy) at the location of fixed point detectors (i.e. loop detector in this study). Traffic propagation was proposed to be predicted based on either a static or dynamic traffic pattern. In the static pattern it was assumed that traffic conditions can be attributed based on the observed conditions in the same time of day; however, in the dynamic pattern, expected traffic conditions are estimated based on the current measurements of upstream and downstream detectors. The prediction results were compared to a naïve method, and it was shown that the average prediction error during the “change period” when traffic conditions are changing from free flow to congestion and vice versa is significantly lower when compared to the naïve method for the sample locations (approximately 25% improvement) For the Traffic Propagation Module, a model has been developed to predict the speed of backward forming and forward recovery shockwaves. Unlike classic shockwave theory which is deterministic, the proposed model expresses the spillback and recovery as a stochastic process. The transition probability matrix is defined as a function of traffic occupancy on upstream and downstream stations in a Markov framework. Then, the probability of spillback and recovery was computed given the traffic conditions. An evaluation using field data has shown that the proposed stochastic model performs better than a classical shockwave model in term of correctly predicting the occurrence of backward forming and forward recovery shockwaves on the field data from the urban expressway. A procedure has been proposed to improve the prediction error of a time series model (Steady-State Module) by using the results of the proposed Markov model. It has been shown that the combined procedure significantly reduces the prediction error of the time series model. For the real-time application of the proposed shockwave model, a module (Congestion Detection Module) is required to simultaneously work with the shockwave model, and identify the state of the traffic based on the available measurements. A model based on Support Vector Machine (SVM) was developed to estimate the current traffic state based on the available information from a fixed point detector. A binary model for the traffic state was considered i.e. free follow versus congested conditions. The model was shown to perform better compared to a Naïve model. The classification model was utilized to inform the Traffic Propagation Module. The combined model showed significant improvement in prediction error of traffic speed during the “Change Period” when traffic conditions are changing from free flow to congestion and vice versa. Variability of travel speed in the near future was also investigated in this research. A continuous-time Markov model has been developed to predict the state of the traffic for the near future. Four traffic states were considered to characterize the state of traffic: two free flow states, one transition state, and one congested state. Using the proposed model, we are able to predict the probability of the traffic being in each of the possible states in the near future based on the current traffic conditions. The predicted probabilities then were utilized to characterize the expected distribution of traffic speed. Based on historical observations, the distribution of traffic speed was characterized for each traffic state separately. Given these empirical distributions and the predicted probabilities, distribution of traffic speed was predicted for the near future. The distribution of traffic speed then was used to predict a confidence interval for the near future. The confidence interval can be used to identify the expected range of future speeds at a given confidence level

대용량 자료를 이용한 네트워크 기반 도시간 경로통행시간 예측

학위논문 (박사)-- 서울대학교 환경대학원 : 환경계획학과, 2015. 2. 이영인.오늘날의 도시교통정보를 한 마디로 요약하자면 대용량 자료일 것이다. 첨단기술을 기반으로 하는 스마트 폰과 21세기 지능형교통체계(Intelligent Transport System, 이하 ITS)의 정보수집단을 통해 수집되는 방대한 양의 다양한 교통정보는 대용량 자료의 많은 부분을 점유하고 있으며, ITS분야의 자료환경을 소량의 실시간 자료에서 방대한 이력자료를 포함하는 대용량 자료로 변화시켰다. 이러한 자료환경의 변화에 따라 최근 ITS분야에서는 대용량 자료를 수집․관리․분석하기 위하여 첨단자료관리시스템(Advanced Data Management System, 이하 ADMS)이 도입되고 있다. ITS의 사회적 편익은 장래 교통상태의 예측을 통한 사전 교통류 관리 및 동적 교통정보제공으로 극대화된다. 따라서 교통상태의 예측은 ITS의 주요 요소 중 하나이며, ITS에서 교통상태 예측시스템은 첨단교통관리시스템과 첨단교통정보시스템의 핵심 하위시스템 중 하나이다. 이와 같이 교통상태 예측기술은 교통상태 예측시스템의 성능과 더불어 ITS의 편익 증대에 있어 밀접한 관련이 있다. 따라서 20세기 말에 ITS가 도입된 이후로 ITS 분야의 예측기술은 다양한 예측모형의 개발을 통해 지속적으로 발전하고 있으며, 하나의 학문분야로 자리잡았다. 기존의 고도화된 예측모형은 예측 정확도 향상이라는 목표를 달성하였으나, 다음의 한계를 가지고 있다. 첫째, 기존모형은 ITS 예측분야의 고질적 문제인 장래 상태의 불확실성을 극복하지 못 하였기 때문에 단기예측의 수준에서 벗어나지 못 하고 있다. 둘째, 많은 경우에 있어 실시간 자료를 이용하도록 설계되었기 때문에 ADMS와 같은 자료관리시스템에 탑재되어 실시간 자료와 대용량 이력자료를 이용하여 교통상태의 예측에 적용하기 어려운 구조적 문제가 있다. 마지막으로 고도화된 모형들은 ITS 시스템에 탑재․운영시 새로운 문제를 발생시키고 있다. 고도화된 모형은 모형의 구조변경, 입․출력 자료의 변경, 파라미터 값의 재정산 등에 교통류의 행태와 예측 모델링에 대한 깊은 이해를 필요로 하기 때문에 예측 모델링 경험이 부족한 현장의 운영요원에게 새로운 장애가 되고 있다. 본 연구에서는 ITS 예측분야의 지속적 도전 과제인 장래 상태의 불확실성을 극복하고 예측영역의 확장을 위하여 실시간 및 대용량 이력 교통자료를 이용한 교통정보 예측기(Forecaster)인 KJC 예측기를 개발하였으며, 다음의 목표를 달성하도록 설계되었다. 첫째, 장래 상태의 불확실성을 극복하기 위한 방안이 고려되었다. 불확실성을 감소시키기 위하여 입력자료의 공간적 영역을 기존의 지점 또는 구간에서 도로망으로 확장하고, 도로망의 소통상태를 이용하여 장래 상태의 불확실성을 감소시켰다. 둘째, KJC 예측기는 ADMS와 같은 자료관리시스템에 탑재되어 도로망 소통상태를 예측하고, 예측된 도로망 소통상태를 이용하여 단․중․장거리 경로통행시간을 예측하도록 개발되었다. 따라서 보다 적극적이고 전술적인 첨단교통관리와 동적 중․장거리 통행시간 정보제공에 활용할 수 있도록 하였다. 이상의 목적으로 개발된 KJC 예측기는 이력자료에 내재된 장래 교통상태 정보를 탐색 및 구축하기 위한 지식탐색 모듈, 군집화 모형을 이용한 의사결정 그룹화 모듈, 그리고 사례기반 추론을 기반으로하는 예측 의사결정 모듈로 구성된다. 3개 모듈은 입․출력 자료구조의 용이한 변경, 결측자료의 자동처리, 파라미터 값의 자동정산, 연산수행속도 등을 고려하여 개발되었다. 본 연구에서 개발된 KJC 예측기의 성능은 대용량 자료환경에서 평가되었다. 경부고속도로 서울-대전 구간을 대상으로 8개월간 약 4억건의 통행사슬 자료를 이용하여 도로구간 소통상태 및 경로통행시간 이력자료를 구축하였으며, 구축된 자료는 총 18,768,960건이다. 다각적인 종합평가 결과, 개발된 교통정보 예측기는 매우 빠른 연산수행속도를 보이면서 장래 6시간까지 도로망의 소통상태를 합리적으로 예측하였다. 그리고 개발된 예측기로 추정된 도시간 경로통행시간의 정확도는 모든 단․중․장거리 통행시간 시나리오에서 기존의 경로통행시간 예측기법들에 비하여 매우 우수하게 나타났다.Abstract Network-based Intercity Path Travel Time Forecasting Using Large-scale Data Chang, Hyun-Ho Department of Environmental Planning The Graduate School of Environmental Studies Seoul National University Todays urban and transportation information can be summarized simply with the words big data. Unimaginably tremendous quantities of information collected by smart-phones and information devices of 21C intelligent transportation systems (ITS) based on edge technology accounts for much of this big data, and changes in the data environment of ITS from small real-time data to big data mean that it now includes vast quantities of historical data as well. With these changes in data environments, advanced data management systems (ADMS) have recently been introduced to process, store, and analyze big data in the field of ITS. The forecasting of traffic conditions along road networks is one of the essential factors with regard to ITS, as the social benefits of ITS can be maximized by proactive traffic management and the provision of dynamic traffic information based on the forecasting of traffic conditions. The traffic state forecasting system is one of the kernel sub-systems of an advanced traffic management system and the advanced traffic information system in ITS. Hence, forecasting technology to generate future states is closely related to increments in ITS benefits and to the performance of the traffic state forecasting system. Various forecasting models, from simple and conventional to refined and sophisticated, have therefore been proposed since ITSs were widely introduced at the end of the twentieth century. Although existing advanced models have essentially achieved the common goal of ITS forecasting with improvements in forecasting accuracy, they have several chronic or emerging problems to be solved. First, the temporal prediction horizon of the models in most cases still operate on the short termthey cannot from the perspective of long-term forecasting overcome uncertainties in future states, and this remains an unsolved problem in the ITS forecasting area. Second, their structures are not suitable when they are coupled with a data management system such as ADMS and then used to estimate future states using both real-time and historical data, as they are in many cases designed to utilize only real-time data. Lastly, many sophisticated models becoming associated with obstacles which require field staff to manage. These models inevitably require the field staff to possess a deep understanding of the behaviors of traffic flows and forecasting model and then to manipulate the operational factors of these entities, such as structural changes of algorithms, in-and-out alterations of data, recalibrations of parameter values and other such actions. In this thesis, a traffic information forecaster termed the KJC forecaster is developed based on a combination of k-nearest neighbor nonparametric regression and j-clustering using both real-time and historical data. First, a conquest solution to address the uncertainties of future states is proposed. In order to reduce the uncertainties of future states, the spatial concept of a forecasting model is expanded from an isolated location or link to a road network, after which traffic states, such as link travel speeds and link probe volumes, of the road network are utilized as the inputs to the forecaster. Second, KJC forecaster is designed to be used in conjunction with data management systems such as ADMS and to estimate the future traffic conditions of road networks. This can in turn be used to generate short-, middle-, and long-distance path travel times. The forecaster, therefore, is at the very least suitable for more proactive and tactical advanced traffic management and especially for dynamic intercity path travel times. The KJC forecaster consists of three modules: a knowledge discovery module to search for and compile the information on future traffic states included in the historical data, a clustering module to determine decision-making groups, and a forecasting decision-making module which is based on case-based reasoning. The three modules were developed while considering operational requirements such as multivariate in-and-out data, easy alterations of inputs and outputs, the automatic processing of missing data, the automatic calibration of parameter values, as well as high-speed computing that is actually faster than real-time. The performance of the traffic information forecaster was tested under the circumstances of large-scale data and the test bed was Seoul-Daejeon road section, 142 km, of Gyeongbu motorway. The historical database used, with a data size of 18,768,960 items, was composed of link-based traffic flow information and path travel times which were compiled using nearly four hundred million instances of trip chain data for eight months. This data was collected by means of dedicated short range communications technology. The results show that KJC forecaster estimates accurate traffic conditions, at least from a forecasting perspective, of road networks up to six hours in the future, with high-speed computations. In addition, the forecaster is clearly superior to two compared path travel time methods, instantaneous and experience-based, in terms of prediction accuracy of the path travel time. Therefore, it is clear that KJC forecaster as proposed in this thesis is a promising multivariate long-term traffic flow forecasting approach which is feasible for use with large-scale data. keywords : Advanced Data Management System, Large-scale Data, k-Nearest Neighbor Nonparametric Regression, j-Clustering, Long-term forecasting, Traffic Condition of Road Network, Intercity Path Travel Time Student Number : 2008-30675Docto

Realistic Cellular Automaton Model for Synchronized Two-Lane Traffic - Simulation, Validation, and Applications

An objective of current traffic-research is the realistic description of vehicular traffic by means of traffic modeling. Since no present traffic model is capable to reproduce all empirical characteristics, the development of such a model is of main interest. Thus, this thesis presents a realistic cellular automaton model for multi-lane traffic and validates it by means of empirical single-vehicle data. In contrast to present approaches a limited deceleration capability is assigned to the vehicles. Moreover, the velocity of the vehicles is determined on the basis of the local neighborhood. Therefore, the drivers are divided into optimistic or pessimistic drivers. The former may underestimate their safety distance if their neighborhood admits it. The latter always keep a safe distance. This results in a convincing reproduction of the microscopic and macroscopic features of synchronized traffic. The anticipation of the leader’s velocity is hereby essential for the reproduction of synchronized traffic. This thesis is divided into three main parts. The first one validates the single-lane model by Lee et al. by means of empirical data. This approach builds the basis for the further developments of this thesis. Then, the fundamental characteristics are summarized. This is followed by new results concerning the comparison with empirical findings that confirm the good reproduction of the reality. The analyses also show the important and fundamental property of synchronized traffic: its density dependent life-time. Nevertheless, accidents appear in the stationary state. Thus, the model approach has to be modified so that it is capable to model multi-lane traffic. The adapted model is enhanced in the next part by a realistic lane change algorithm. A multi-lane model is formulated that reproduces the empirical data even better than the single-lane approach. Moreover, specific two-lane characteristics like the density dependent lane change frequency are reproduced as well as the coupling of the lanes. Moreover, if the velocity difference between the two lanes is too high, the lanes may decouple, i.e., different traffic states emerge on the two lanes. This is a direct consequence of the limited deceleration capability of the vehicles. In the last part of the thesis the two-lane model is applied to open systems with bottlenecks like an on-ramp and a speed-limit. The empirically observed complex structures of the synchronized traffic are reproduced here in great detail. Thus, the approach discussed in this thesis exceeds the present in the degree of realism. Because of the reliability of the presented model it is supposed to be implemented to simulate the whole network of North Rhine-Westphalia.Ziel der aktuellen Verkehrsforschung ist es, den Straßenverkehr durch Verkehrsmodelle realitätsnah zu beschreiben. Da kein bisheriges Modell alle empirisch beobachteten Phänomene exakt nachbilden kann, ist dessen Entwicklung ein aktueller Forschungsschwerpunkt. Daher präsentiert die vorliegende Arbeit ein realitätsnahes Zellularautomaten-Modell für den mehrspurigen Straßenverkehr und validiert dieses anhand empirischer Einzelfahrzeug-Daten. Im Unterschied zu den bisherigen Modellansätzen wird ein begrenztes Bremsvermögen für die Fahrzeuge eingeführt. Darüber hinaus wird die Geschwindigkeit auf Basis der Umgebung des Fahrers bestimmt und die Fahrer in optimistische und pessimistische eingeteilt. Die ersteren können den Sicherheitsabstand unterschreiten, wenn die Umgebung dies zulässt, die letzteren halten dagegen einen sicheren Abstand ein. Im Ergebnis bildet das Modell die mikroskopischen und makroskopischen Eigenschaften des synchronisierten Verkehrs überzeugend nach. Die Antizipation der Geschwindigkeit des vorausfahrenden Fahrzeugs ist dabei für die Reproduktion des synchronisierten Verkehrs wesentlich. Die Arbeit besteht aus drei Teilen. Der erste Teil verifiziert das Einspur-Modell von Lee et al anhand von empirischen Daten, welcher das Basismodell für die weiteren Entwicklungen bildet und dessen grundlegende Eigenschaften zusammengefasst werden. Es folgen neue Ergebnisse aus dem Vergleich zu den empirischen Daten, welche die gute Übereinstimmung mit der Realität bestätigen. Die Analyse des synchronisierten Verkehrs offenbart eine wesentliche Eigenschaft: die Dichteabhängigkeit der Lebensdauer. Da im ursprünglichen Modellansatz Unfälle auftreten können, muss dieses für die Modellierung von Mehrspur-Verkehr auf größere Unfallfreiheit zugeschnitten werden. Das angepasste Modell wird im nächsten Teil um einen realitätsnahen Spurwechselalgorithmus erweitert. Es wird ein wirklichkeitsnahes Modell formuliert, dass die empirischen Daten eines zweispurigen Abschnitts besser reproduziert als das bisherige Einspur-Modell. Der Modellansatz bildet die für den Zweispur-Verkehr spezifischen Charakteristiken nach, insbesondere die dichteabhängige Spurwechselrate. Darüber hinaus sind nebeneinander liegende Fahrspuren bei großen Geschwindigkeitsunterschieden nicht gekoppelt, was eine direkte Konsequenz des beschränkten Bremsvermögens ist. Im letzten Teil dieser Arbeit wird das Zweispur-Modell auf offene Systeme mit Engpässen wie Zufahrten und Geschwindigkeitsbegrenzungen angewendet. Die in der Realität beobachteten komplexen Strukturen des synchronisierten Verkehrs werden hier sehr detailliert nachgebildet, so dass das vorgestellte Modell die bisherigen Ansätze an Realitätsnähe übertrifft. Aufgrund der Zuverlässigkeit des Modells wird in der weitergehenden Forschung der Ansatz auf das gesamte Autobahnnetz des Landes Nordrhein-Westfalen angewendet